Directionally I think this is right. Most LLM usage at scale tends to be filling the gaps between two hardened interfaces. The reliability comes not from the LLM inference and generation but the interfaces themselves only allowing certain configuration to work with them.
LLM output is often coerced back into something more deterministic such as types, or DB primary keys. The value of the LLM is determined by how well your existing code and tools model the data, logic, and actions of your domain.
In some ways I view LLMs today a bit like 3D printers, both in terms of hype and in terms of utility. They excel at quickly connecting parts similar to rapid prototyping with 3d printing parts. For reliability and scale you want either the LLM or an engineer to replace the printed/inferred connector with something durable and deterministic (metal/code) that is cheap and fast to run at scale.
Additionally, there was a minute during the 3D printer Gardner hype cycle where there were notions that we would all just print substantial amounts of consumer goods when the reality is the high utility use case are much more narrow. There is a corollary here to LLM usage. While LLMs are extremely useful we cannot rely on LLMs to generate or infer our entire operational reality or even engage meaningfully with it without some sort of pre-existing digital modeling as an anchor.
Hype cycle for drones and VR was similar -- at the peak, you have people claiming drones will take over package delivery and everyone will spend their day in VR. Reality is that the applicability is more narrow.
That's the claim for AR, not VR, and you're just noticing how research and development cycles play out, you can draw comparisons to literally any technology cycle.
Good drones are very Chinese atm, as is casual consumer drone delivery. Americans might be more than a decade away even with concerted bipartisan war-like effort to boost domestic drone competency.
I mean both of these things are actually happening (drone deliveries and people spending a lot of time in VR), just at a much much smaller scale than it was hyped up to be.
Drones and VR require significant upfront hardware investment, which curbs adoption. On the other hand, adopting LLM-as-a-service has none of these costs, so no wonder so many companies are getting involved with it so quickly.
Right, but abstract costs are still costs to someone, so how far does that go before mass adoption turns into a mass liability for whomever is ultimately on the hook? It seems like there is this extremely risky wager that everyone is playing--that LLM's will find their "killer app" before the real costs of maintaining them becomes too much to bear. I don't think these kinds of bets often pay off. The opposite actually, I think every truly revolutionary technological advance in the contemporary timeframe has arisen out of its very obvious killer app(s), they were in a sense inevitable. Speculative tech--the blockchain being one of the more salient and frequently tapped examples--tends to work in pretty clear bubbles, in my estimation. I've not yet been convinced this one is any different, aside from the absurd scale at which it has been cynically sold as the biggest thing since Gutenberg, but while that makes it somewhat distinct, it's still a rather poor argument against it being a bubble.
Considering what we've been seeing in the Russia-Ukraine and Iran-Israel wars, drones are definitely happening at scale. For better or for worse, I expect worldwide production of drones to greatly expand over the coming years.
This makes no sense, just because something didn't become as big as the hypemen said it would doesn't make the inventions or users of those inventions disappear.
For something to be considered “happening” you can’t just have a handful of localized examples. It has to be happening at a large noticeable scale that even people unfamiliar with the tech are noticing. Then you can say it’s “happening”. Otherwise, it’s just smaller groups of people doing stuff.
You checked out drone warfare? It’s all the rage in every conflict at the moment. The hype around drones is not fake, and I’d compare it more to autonomous cars because regulation is the only reason you don’t see a million private drones flying around.
Interesting take but too bearish on LLMs in my opinion.
LLMs have already found large-scale usage (deep research, translation) which makes them more ubiquitous today than 3D printers ever will or could have been.
What we call an LLM today (by which almost everyone means an autogressive language model from the Generative Pretrained Transformer family tree, and BERTs are still doing important eork, believe that) is actually an offshoot of neural machine translation.
This isn't (intentionally at least) mere HN pedantry: they really do act like translation tools in a bunch of observable ways.
And while they have recently crossed the threshold into "yeah, I'm always going to have a gptel buffer open now" territory at the extreme high end, their utility outside of the really specific, totally non-generalizing code lookup gizmo usecase remains a claim unsupported by robust profits.
There is a hole in the ground where something between 100 billion and a trillion dollars in the ground that so far has about 20B in revenue (not profit) going into it annually.
AI is going to be big (it was big ten years ago).
LLMs? Look more and more like the Metaverse every day as concerns the economics.
I said as concerns the economics. It's clearly more popular than the Oculus or whatever, but it's still a money bonfire and shows no signs of changing on that front.
> There is a hole in the ground where something between 100 billion and a trillion dollars in the ground that so far has about 20B in revenue (not profit) going into it annually.
This is a concern for me. I'm using claude-code daily and find it very useful, but I'm expecting the price to continue getting jacked up. I do want to support Anthropic, but they might eventually need to cross a price threshold where I bail. We'll see.
I expect at some point the more open models and tools will catch up when the expensive models like ChatGPT plateau (assuming they do plateau). Then we'll find out if these valuations measure up to reality.
Note to the Hypelords: It's not perfect. I need to read every change and intervene often enough. "Vibe coding" is nonsense as expected. It is definitely good though.
I'm just taking advantage and burning VCs' money on useful but not world-changing tools while I still can. We'll come out of it with consumer-level okay tools even if they don't reach the levels of Claude today, though.
As a thought-exercise -- assume models continue to improve, whereas "using claude-code daily" is something you choose to do because it's useful, but is not yet at the level of "absolute necessity, can't imagine work without it". What if it does become, that level of absolute necessity?
- Is your demand inelastic at that point, if having claude-code becomes effectively required, to sustain your livelihood? Does pricing continue to increase, until it's 1%/5%/20%/50% of your salary (because hey, what's the alternative? if you don't pay, then you won't keep up with other engineers and will just lose your job completely)?
- But if tools like claude-code become such a necessity, wouldn't enterprises be the ones paying? Maybe, but maybe like health-insurance in America (a uniquely dystopian thing), your employer may pay some portion of the premiums, but they'll also pass some costs to you as the employee... Tech salaries have been cushy for a while now, but we might be entering a "K-shaped" inflection point --> if you are an OpenAI elite researcher, then you might get a $100M+ offer from Meta; but if you are an average dev doing average enterprise CRUD, maybe your wages will be suppressed because the small cabal of LLM providers can raise prices and your company HAS to pay, which means you HAVE to bear the cost (or else what? you can quit and look for another job, but who's hiring?)
This is a pessimistic take of course (and vastly oversimplified / too cynical). A more positive outcome might be, that increasing quality of AI/LLM options leads to a democratization of talent, or a blossoming of "solo unicorns"... personally I have toyed with calling this, something like a "techno-Amish utopia", in the sense that Amish people believe in self-sufficiency and are not wholly-resistant to technology (it's actually quite clever, what sorts of technology they allow for themselves or not), so what if we could take that further?
If there was a version of that Amish-mentality of loosely-federated self-sufficient communities (they have newsletters! they travel to each other! but they largely feed themselves, build their own tools, fix their own fences, etc.!), where engineers + their chosen LLM partner could launch companies from home, manage their home automation / security tech, run a high-tech small farm, live off-grid from cheap solar, use excess electricity to Bitcoin mine if they choose to, etc.... maybe there is actually a libertarian world that can arise, where we are no longer as dependent on large institutions to marshal resources, deploy capital, scale production, etc., if some of those things are more in-reach for regular people in smaller communities, assisted by AI. This of course assumes that, the cabal of LLM model creators can be broken, that you don't need to pay for Claude if the cheaper open-source-ish Llama-like alternative is good enough
Vibe coding is nonsense, and its really kind of uncomfortable to realize that a bunch of people you had tons of respect for are either ignorant or dishonest/bought enough to say otherwise. There's a cold wind blowing and the bunker-building crowd, well let's just say I won't shed a tear.
You don't stock antibiotics and bullets in a survival compound because you think that's going to keep out a paperclip optimizer gone awry. You do that in the forlorn hope that when the guillotines come out that you'll be able to ride it out until the Nouveau Regime is in a negotiating mood. But they never are.
Without trying to take away from your assertion, I think it is worthwhile to mention that part of this phenomenon is the unavoidable matter of meatspace being expensive and dataspace being intangibly present everywhere.
And yet you didn't provide a single reference link! Every case of LLM usage that I've seen claimed about those things has been largely a lie -- guess you won't take the opportunity to be the first to present a real example. Just another rumor.
Outside of tech circles it looks like NFTs: people following hype using tech they don't understand which will be popular until the downsides we're aware of that they are ignorant to have consequences, and then the market will reflect the shift in opinion.
Everybody under a certain age is using ChatGPT, where they were once using search and friendship/expertises. It’s the number 1 app in the App Store. Copilot use in the enterprise is so seamless, you just talk to PowerPoint or outlook and it formulated what you were supposed to make or write.
It’s not a fad, it is a paradigm change.
People don’t need to understand how it works for it to work.
Even if the most bearish predictions turn out to be correct, the comparison of LLMs to NFTs is a galaxy-spanning stretch.
NFTs are about as close to literally useless as it gets, and that was always obvious; 99% of the serious attention paid to them came from hustlers and speculators.
LLMs, for all their limitations, are already good at some things and useful in some ways. Even in the areas where they are (so far) too unreliable for serious use, they're not pure hype and bullshit; they're doing things that would have seemed like magic 10 years ago.
Not even remotely in the same universe; the difference is ChatGPT is actually having an impact, people are incorporating it day-to-day in a way that NFTs never stood much of a chance.
No, 3D printers are the backbone of modern physical prototyping. They're far more important to today's global economy than LLMs are, even if you don't have the vantage point to see it from your sector. That might change in the future, but snapping your fingers to wink LLMs out of existence would change essentially nothing about how the world works today; it would be a non-traumatic non-event. There just hasn't been time to integrate them into any essential processes.
No, not even close. By 2006 all sorts of load-bearing infrastructure was relying on Google (e.g. Gmail). Today LLMs are still on the edge of important systems, rather than underlying those systems.
Things like BERT are a load bearing structure in data science pipelines.
I assume there are massive number of LLM analysis pipelines out there.
I suppose it depends if you consider non determinist DS/ML pipelines "loadbearing" or not. Most are not using LLMs though.
3D parts regularly are used beyond prototyping though as tooling for a small company can be higher than just metal 3D parts. So I do somewhat agree but the loss of productivity in software prototyping would be a massive hit if LLMs vanished.
Something I've realized about LLM tool use is that it means that if you can reduce a problem to something that can be solved by an LLM in a sandbox using tools in a loop, you can brute force that problem.
The job then becomes identifying those problems and figuring out how to configure a sandbox for them, what tools to provide and how to define the success criteria for the model.
That still takes significant skill and experience, but it's at a higher level than chewing through that problem using trial and error by hand.
> The job then becomes identifying those problems and figuring out how to configure a sandbox for them, what tools to provide, and how to define the success criteria for the model.
Your test case seems like a quintessential example where you're missing that last step.
Since it is unlikely that you understand the math behind fractals or x86 assembly (apologies if I'm wrong on this), your only means for verifying the accuracy of your solution is a superficial visual inspection, e.g. "Does it look like the Mandelbrot series?"
Ideally, your evaluation criteria would be expressed as a continuous function, but at the very least, it should take the form of a sufficiently diverse quantifiable set of discrete inputs and their expected outputs.
I’ve been thinking about using LLMs for brute forcing problems too.
Like LLMs kinda suck at typescript generics. They’re surprisingly bad at them. Probably because it’s easy to write generics that look correct, but are then screwy in many scenarios. Which is also why generics are hard for humans.
If you could have any LLM actually use TSC, it could run tests, make sure things are inferring correctly, etc. it could just keep trying until it works. I’m not sure this is a way to produce understandable or maintainable generics, but it would be pretty neat.
Also while typing this is realized that cursor can see typescript errors. All I need are some utility testing types, and I could have cursor write the tests and then brute force the problem!
If I ever actually do this I’ll update this comment lol
Giving LLMs the right context -- eg in the form of predefined "cognitive tools", as explored with a ton of rigor
here^1 -- seems like the way forward, at least to this casual observer.
> LLM in a sandbox using tools in a loop, you can brute force that problem
Does this require using big models through their APIs and spending a lot of tokens?
Or can this be done either with local models (probably very slow), or with subscriptions like Claude Code with Pro (without hitting the rate/usage limits)?
I saw the Mandelbrot experiment, it was very cool, but still a rather small project, not really comparable to a complex/bigger/older code base for a platform used in production
The local models aren't quite good enough for this yet in my experience - the big hosted models (o3, Gemini 2.5, Claude 4) only just crossed the capability threshold for this to start working well.
I think it's possible we'll see a local model that can do this well within the next few months though - it needs good tool calling, not an encyclopedic knowledge of the world. Might be possible to fit that in a model that runs locally.
> it needs good tool calling, not an encyclopedic knowledge of the world
I wonder if there are any groups/companies out there building something like this
Would love to have models that only know 1 or 2 languages (eg. python + js), but are great at them and at tool calling. Definitely don't need my coding agent to know all of Wikipedia and translating between 10 different languages
1. A special code dataset
2. A bunch of "unrelated" books
My understanding is that the model trained on just the first will never beat the model trained on both. Bloomberg model is my favorite example of this.
If you can squirell away special data then that special data plus everything else will beat the any other models. But that's basically what openai, google, and anthropic are all currently doing.
Wasn't there a tool calling benchmark by docker guys which concluded qwen models are nearly as good as GPT? What is your experience about it?
Personally I am convinced JSON is a bad format for LLMs and code orchestration in python-ish DSL is the future. But local models are pretty bad at code gen too.
There's a fine-tune of Qwen3 4B called "Jan Nano" that I started playing with yesterday, which is basically just fine-tuned to be more inclined to look things up via web searches than to answer them "off the dome". It's not good-good, but it does seem to have a much lower effective hallucination rate than other models of its size.
It seems like maybe similar approaches could be used for coding tasks, especially with tool calls for reading man pages, info pages, running `tldr`, specifically consulting Stack Overflow, etc. Some of the recent small MoE models from Chinese companies are significantly smarter than models like Qwen 4B, but run about as quickly, so maybe on systems with high RAM or high unified memory, even with middling GPUs, they could be genuinely useful for coding if they are made to be avoid doing anything without tool use.
I've been using a VM for a sandbox, just to make sure it won't delete my files if it goes insane.
With some host data directories mounted read only inside the VM.
This creates some friction though. Feels like a tool which runs the AI agent in a VM, but then copies it's output to the host machine after some checks would help, so that it would feel that you are running it natively on the host.
This is very easy to do with Docker. Not sure it you want the vm layer as an extra security boundary, but even so you can just specify the VM’s docker api endpoint to spawn processes and copy files in/out from shell scripts.
Hmm, excellent idea, somehow I assumed that it would be able to do damage in a writable volume, but it wouldn't be able to exit it, it would be self-contained to that directory.
I have the feeling that's not really MCP specifically VS other ways, but it is pretty simply: at the current state of AI, to have a human in the loop is much better. LLMs are great at certain tasks but they often get trapped into local minima, if you do the back and forth via the web interface of LLMs, ask it to write a program, look at it, provide hints to improve it, test it, ..., you get much better results and you don't cut yourself out to find a 10k lines of code mess that could be 400 lines of clear code. That's the current state of affairs, but of course many will try very hard to replace programmers that is currently not possible. What it is possible is to accelerate the work of a programmer several times (but they must be good both at programming and LLM usage), or take a smart person that has a relatively low skill in some technology, and thanks to LLM make this person productive in this field without the long training otherwise needed. And many other things. But "agentic coding" right now does not work well. This will change, but right now the real gain is to use the LLM as a colleague.
It is not MCP: it is autonomous agents that don't get feedbacks from smart humans.
So I run my own business (product), I code everything, and I use claude-code. I also wear all the other hats and so I'd be happy to let Claude handle all of the coding if / when it can. I can confirm we're certainly not there yet.
It's definitely useful, but you have to read everything. I'm working in a type-safe functional compiled language too. I'd be scared to try this flow in a less "correctness enforced" language.
That being said, I do find that it works well. It's not living up to the hype, but most of that hype was obvious nonsense. It continues to surprise me with it's grasp on concepts and is definitely saving me some time, and more importantly making some larger tasks more approachable since I can split my time better.
> try completing a GitHub task with the GitHub MCP, then repeat it with the gh CLI tool. You'll almost certainly find the latter uses context far more efficiently and you get to your intended results quicker.
This is spot on. I have a "devops" folder with a CLAUDE.md with bash commands for common tasks (e.g. find prod / staging logs with this integration ID).
When I complete a novel task (e.g. count all the rows that were synced from stripe to duckdb) I tell Claude to update CLAUDE.md with the example. The next time I ask a similar question, Claude one-shots it.
This is the first few lines of the CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
## Purpose
This devops folder is dedicated to Google Cloud Platform (GCP) operations, focusing on:
- Google Cloud Composer (Airflow) DAG management and monitoring
- Google Cloud Logging queries and analysis
- Kubernetes cluster management (GKE)
- Cloud Run service debugging
## Common DevOps Commands
### Google Cloud Composer
```bash
# View Composer environment details
gcloud composer environments describe meltano --location us-central1 --project definite-some-id
# List DAGs in the environment
gcloud composer environments storage dags list --environment meltano --location us-central1 --project definite-some-id
# View DAG runs
gcloud composer environments run meltano --location us-central1 dags list
# Check Airflow logs
gcloud logging read 'resource.type="cloud_composer_environment" AND resource.labels.environment_name="meltano"' --project definite-some-id --limit 50
I feel like I'm taking crazy pills sometimes. You have a file with a set of snippets and you prefer to ask the AI to hopefully run them instead of just running it yourself?
The commands aren't the special sauce, it's the analytical capabilities of the LLM to view the outputs of all those commands and correlate data or whatever. You could accomplish the same by prefilling a gigantic context window with all the logs but when the commands are presented ahead of time the LLM can "decide" which one to run based on what it needs to do.
the snippets are examples. You can ask hundreds of variations of similar, but different, complex questions and the LLM can adjust the example for that need.
I don't have a snippet for, "find all 500's for the meltano service for duckdb syntax errors", but it'd easily nail that given the existing examples.
but if I know enough about the service to write examples, most of the time I will know the command I want, which is less typing, faster, costs less, and doesn't waste a ton of electricity.
In the other cases I see what the computer outputs, LEARN, and then the functionality of finding what I need just isn't useful next time. Next time I just type the command.
LLMs are really good at processing vague descriptions of problems and offering a solution that's reasonably close to the mark. They can be a great guide for unfamiliar tools.
For example, I have a pretty good grasp of regular expressions because I'm an old Perl programmer, but I find processing json using `jq` utterly baffling. LLMs are great at coming up with useful examples, and sometimes they'll even get it perfect the first time. I've learned more about properly using `jq` with the help of LLMs than I ever did on my own. Same goes for `ffmpeg`.
LLMs are not a substitute for learning. When used properly, they're an enhancement to learning.
Likewise, never mind the idiot CEOs of failing companies looking forward to laying off half their workforce and replacing them with AI. When properly used, AI is a tool to help people become more productive, not replace human understanding.
Yes. I'm not the poster but I do something similar.
Because now the capabilities of the model grow over time. And I can ask questions that involve a handful of those snippets. When we get to something new that requires some doing, it becomes another snippet.
I can offload everything I used to know about an API and never have to think about it again.
Just as a related aside, you could literally make that bottom section into a super simple stdio MCP Server and attach that to Claude Code. Each of your operations could be a tool and have a well-defined schema for parameters. Then you are giving the LLM a more structured and defined way to access your custom commands. I'm pretty positive there are even pre-made MCP Servers that are designed for just this activity.
No. You are giving textual instructions to Claude in the hopes that it correctly generates a shell command for you vs giving it a tool definition with a clearly defined schema for parameters and your MCP Server is, presumably, enforcing adherence to those parameters BEFORE it hits your shell. You would be helping Claude in this case as you're giving a clearer set of constraints on operation.
Well, with MCP you’re giving textual instructions to Claude in hopes that it correctly generates a tool call for you. It’s not like tool calls have access to some secret deterministic mode of the LLM; it’s still just text.
To an LLM there’s not much difference between the list of sample commands above and the list of tool commands it would get from an MCP server. JSON and GNU-style args are very similar in structure. And presumably the command is enforcing constraints even better than the MCP server would.
Either way it is text instructions used to call a function (via a JSON object for MCP or a shell command for scripts). What works better depends on how the model you’re using was post trained and where in the prompt that info gets injected.
I do something similar, but the problem is that claude.md keeps on growing.
To tackle this, I converted a custom prompt into an application, but there is an interesting trade-off. The application is deterministic. It cannot deal with unknown situations. In contrast to CC, which is way slower, but can try alternative ways of dealing with an unknown situation.
I ended up with adding an instruction to the custom command to run the application and fix the application code (TDD) if there is a problem. Self healing software… who ever thought
I use a similar file, but just for myself (I've never used an LLM "agent"). I live in Emacs, but this is the only thing I use org-mode for: it lets me fold/unfold the sections, and I can press C-c C-c over any of the code snippets to execute it. Some of them are shell code, some of them are Emacs Lisp code which generates shell code, etc.
My absolute favorite use of MCP so far is Bruce Hauman's clojure-mcp. In short, it gives the LLM (a) a bash tool, (b) a persistent Clojure REPL, and (c) structural editing tools.
The effect is that it's far more efficient at editing Clojure code than any purely string-diff-based approach, and if you write a good test suite it can rapidly iterate back and forth just editing files, reloading them, and then re-running the test suite at the REPL -- just like I would. It's pretty incredible to watch.
I was just going to comment about clojure-mcp!! It’s far and away the coolest use of mcp I’ve seen so far.
It can straight up debug your code, eval individual expressions, document return types of functions. It’s amazing.
It actually makes me think that languages with strong REPLs are a better for languages than those without. Seeing clojure-mcp do its thing is the most impressive AI feat I’ve seen since I saw GPT-3 in action for the first time
The problem I see with MCP is very simple. It's using JSON as the format and that's nowhere as expressive as a programming language.
Consider a python function signature
list_containers(show_stopped: bool = False, name_pattern: Optional[str] = None, sort: Literal["size", "name", "started_at"] = "name"). It doesn't even need docs
Now convert this to JSON schema which is 4x larger input already.
And when generating output, the LLM will generate almost 2x more tokens too, because JSON. Easier to get confused.
And consider that the flow of calling python functions and using their output to call other tools etc... is seen 1000x more times in their fine tuning data, whereas JSON tool calling flows are rare and practically only exist in instruction tuning phase. Then I am sure instruction tuning also contains even more complex code examples where model has to execute complex logic.
Then theres the whole issue of composition. To my knowledge there's no way LLM can do this in one response.
vehicle = call_func_1()
if vehicle.type == "car":
details = lookup_car(vehicle.reg_no)
else if vehicle.type == "motorcycle":
details = lookup_motorcycle(vehicle.reg_ni)
the reason to use the llm is that you dont know ahead of time that the vehicle type is only a car or motorcycle, and the llm will also figure out a way to detail bycicles and boats and airplanes, and to consider both left and right shoes separately.
the llm cant just be given this function because its specialized to just the two options.
you could have it do a feedback loop of rewriting the python script after running it, but whats the savings at tha point? youre wasting tokens talking about cars in python when you already know is a ski, and the llm could ask directly for the ski details without writing a script to do it in between
But "the" problem with MCP? IMVHO (Very humble, non-expert) the half-baked or missing security aspects are more fundamental. I'd love to hear updates about that from ppl who know what they're talking about.
Regarding the Playwright example: I had the same experience this week attempting to build an agent first by using the Playwright MCP server, realizing it was slow, token-inefficient, and flaky, and rewriting with direct Playwright calls.
MCP servers might be fun to get an idea for what's possible, and good for one-off mashups, but API calls are generally more efficient and stable, when you know what you want.
This is solved trivially by having default initial prompts.
All majors tools like Claude Code or Gemini CLI have ways to set them up.
> You pass all your tools to an LLM and ask it to filter it down based on the task at hand. So far, there hasn't been much better approaches proposed.
Why is a "better" approach needed? If modern LLMs can properly figure it out? It's not like LLMs don't keep getting better with larger and larger context length.
I never had a problem with an LLM struggling to use the appropriate MCP function on it's own.
> But you run into three problems: cost, speed, and general reliability
- cost: They keep getting cheaper and cheaper. It's ridiculously inexpensive for what those tools provide.
- speed: That seem extremely short sighted. No one is sitting idle looking at Claude Code in their terminal. And you can have more than one working on unrelated topics. That defeats the purpose. No matter how long it takes the time spent is purely bonus. You don't have to spend time in the loop when asking well defined tasks.
- reliability: Seem very prompt correlated ATM. I guess some people don't know what to ask which is the main issue.
Having LLMS being able to complete tedious tasks involving so many external tools at once is simply amazing thanks to MCP. Anecdotal but just today it did a task flawlessly involving: Notion pages, Linear Ticket, git, GitHub PR, GitHub CI logs.
Being in the loop was just submitting one review on the PR. All the while I was busy doing something else. And for what, ~1$?
> This is solved trivially by having default initial prompts. All majors tools like Claude Code or Gemini CLI have ways to set them up.
That only makes it worse. The MCP tools available all add to the initial context. The more tools, the more of the context is populated by MCP tool definitions.
Do you mean that some tools (MCP clients) pass all functions of all configured MCP servers in the initial prompt?
If that's the case:
I understand the knee-jerk reaction but if it works?
Also what theoretically prevents altering the prompt chaining logic in these tools to only expose a condensed list of MCP servers, not their whole capabilities, and only inject details based on LLM outputs?
Doesn't seem like an insurmountable problem.
> Do you mean that some tools (MCP clients) pass all functions of all configured MCP servers in the initial prompt?
Not just some, all. That's just how MCP works.
> If that's the case: I understand the knee-jerk reaction but if it works?
I would not be writing about this if it worked well. The data indicates that it worse significantly worse than not using MCP because of the context rot, and the low too utilization.
"Cursor released a $200-a-month subscription then made their $20-a-month subscription worse (worse output, slower) - yet it seems even on Max they're rate limiting people!"
I have used MCP daily for a few months. I'm now down to a single MCP server: terminal (iTerm2). I have OpenAPI specs on hand if I ever need to provide them, but honestly shell commands and curl get you pretty damn far.
I think the GitHub CLI example isn't entirely fair to MCP. Yes, GitHub's CLI is extensively documented online, so of course LLMs will excel at generating code for well-known tools. But MCP shines in different scenarios.
Consider internal company tools or niche APIs with minimal online documentation. Sure, you could dump all the documentation into context for code generation, but that often requires more context than interacting with an MCP tool. More importantly, generated code for unfamiliar APIs is prone to errors so you'd need robust testing and retry mechanisms built in to the process.
With MCP, if the tools are properly designed and receive correct inputs, they work reliably. The LLM doesn't need to figure out API intricacies, authentication flows, or handle edge cases - that's already handled by the MCP server.
So I agree MCP for GitHub is probably overkill but there are many legitimate use cases where pre-built MCP tools make more sense than asking an LLM to reverse-engineer poorly documented or proprietary systems from scratch.
> Sure, you could dump all the documentation into context for code generation, but that often requires more context than interacting with an MCP tool.
MCP works exactly that way: you dump documentation into the context. That's how the LLM knows how to call your tool. Even for custom stuff I noticed that giving the LLM things to work with that it knows (eg: python, javascript, bash) beats it using MCP tool calling, and in some ways it wastes less context.
YMMV, but I found the limit of tools available to be <15 with sonnet4. That's a super low amount. Basically the official playwright MCP alone is enough to fully exhaust your available tool space.
Ive never used that many. The LLM performances collapse/degrade significantly because of too much initial context?
It seems like MCP implems updates could easily solve that. Like only injecting relevant servers for the given task based on initial user prompt.
That's handled by the MCP server in the sense of it doesn't do authentication, etc. it provides a simplified view of the world.
If that's what you wanted you could have designed that as your poorly documented internal API differently to begin with. There's zero advantage to MCP in the scenario you describe aside from convincing people that their original API is too hard to use.
> So maybe we need to look at ways to find a better abstraction for what MCP is great at, and code generation. For that that we might need to build better sandboxes and maybe start looking at how we can expose APIs in ways that allow an agent to do some sort of fan out / fan in for inference. Effectively we want to do as much in generated code as we can, but then use the magic of LLMs after bulk code execution to judge what we did.
Por que no los dos? I ended up writing an OSS MCP server that securely executes LLM generated JavaScript using a C# JS interpreter (Jint) and handing it a `fetch` analogue as well as `jsonpath-plus`. Also gave it a built-in secrets manager.
Give it an objective and the LLM writes its own code and uses the tool iteratively to accomplish the task (as long as you can interact with it via a REST API).
For well known APIs, it does a fine job generating REST API calls.
Yup, I can't help but think that a lot of the bad thinking comes from trying to avoid the following fact: LLMs are only good where your output does not need to be precise and/or verifiably "perfect," which is kind of the opposite of how code has worked, or has tried to work, in the past.
Right now I got it for: DRAFTS of prose things -- and the only real killer in my opinion, autotagging thousands of old bookmarks. But again, that's just to have cool stuff to go back and peruse, not something that must be correc.t
Wouldn't the sweet spot for MCP be where the LLM is able to do most of the heavy lifting on its own (outputting some kind of structured or unstructured output), but needs a bit of external/dynamic data that it can't do without? The list of MCP servers/tools it can use should nail that external lookup in a (mostly) deterministic way.
This would work best if a human is the end consumer of this output, or will receive manual vetting eventually. I'm not sure I'd leave such a system running unsupervised in production ("the Automation at Scale" part mentioned by the OP).
I agree. In any large backend software running on a server, it's the LLM invocation which would be a call out to an external system, and with proper validation around the results. At which point, calling an "MCP Server" is also just your backend software invoking one more library/service based on inspecting some part of the response from the LLM.
This doesn't take away from the utility of MCP when it comes Claude Desktop and the likes!
This is similar to the tool call (fixed code & dynamic params) vs code generation (dynamic code & dynamic params) discussion: tools offer contrains and save tokens, code gives you flexibility. Some papers suggest that generating code is often superior and this will likely become even more true as language models improve
I am working on a model that goes a step beyond and even makes the distinction between thinking and code execution unnecessary (it is all computation in the end), unfortunately no link to share yet
You can combine MCPs within composable LLM generated code if you put in a little work. At Continual (https://continual.ai), we have many workflows that require bulk actions, e.g. iterating over all issues, files, customers, etc. We inject MCP tools into a sandboxed code interpreter and have the agent generate both direct MCP tool calls and composable scripts that leverage MCP tools depending on the task complexity. After a bunch of work it actually works quite well. We are also experimenting with continual learning via a Voyager like approach where the LLM can save tool scripts for future use, allowing lifelong learning for repeated workflows.
That autocompounding aspect of constantly refining initial prompts with more and more knowledge is so interesting.
Gut feeling says it’s something that will be “standardized” in some way, exactly like what MCP did.
Yes, I think you could get quite far with a few tools like memory/todo list + code interpreter + script save/load. You could probably get a lot farther though if you RLVRed this similar to how o3 uses web search so effectively during it's thinking process.
I always dreamed of a tool which would know the intent, semantic and constraints of all inputs and outputs of any piece of code and thus could combine these code pieces automatically. It was always a fuzzy idea in my head, but this piece now made it a bit more clear. While LLMs could generate those adapters between distinct pieces automatically, it's a expensive (latency, tokens) process. Having a system with which not only to type the variables, but also to type the types (intents, semantic meaning, etc.) would be helpful but likely not sufficient. There has been so much work on ontologies, semantic networks, logical inference, etc. but all of it is spread all over the place. I'd like to have something like this integrated into a programming language and see what it feels like.
We’re playing an endless cat and mouse game of capabilities between old and new right now.
Claude Code shows that the models can excel at using “old” programmatic interfaces (CLIs) to do Real Work™.
MCP is a way to dynamically provide “new” programmatic interfaces to the models.
At some point this will start to converge, or at least appear to do so, as the majority of tools a model needs will be in its pre-training set.
Then we’ll argue about MPPP (model pre-training protocol pipeline), and how to reduce knowledge pollution of all the LLM-generated tools we’re passing to the model.
Eventually we’ll publish the Merrium-Webster Model Tool Dictionary (MWMTD), surfacing all of the approved tools hidden in the pre-training set.
Then the kids will come up with Model Context Slang (MCS), in an attempt to use the models to dynamically choose unapproved tools, for much fun and enjoyment.
MCP is literally the same as giving an LLM a set of man page summaries and a very limited shell over HTTP. It’s just in a different syntax (JSON instead of man macros and CLI args).
It would be better for MCP to deliver function definitions and let the LLM write little scripts in a simple language.
Tools are constraints and time/token savers. Code is expensive in terms of tokens and harder to constrain in environments that can’t be fully locked-down because network access for example is needed by the task. You need code AND tools.
Honestly, I’m getting tired of these sweeping statements about what developers are supposed to be, how it’s “the right way to use AI”. We are in uncharted territories that are changing by the day. Maybe we have to drop the self-assurance and opinionated view points and tackle this like a scientific problem.
100% agreed - he mentions 3 barriers to using MCP over code: "cost, speed, and general reliability". But all 3 of these could change by 10-100x within a few years, if not months. Just recently OpenAI dropped the price of using o3 by 80%
This is not an environment where you can establish a durable manifesto
I think the Playwright MCP is a really good example of the overall problem that the author brings up.
However, I couldn’t really understand if he’s saying that the Playwright MCP is good to use for your own app or whether he means for your own app just tell the LLM directly to export Playwright code.
it's the latter: "you can actually start telling it to write a Playwright Python script instead and run that".
and while running the code might faster, it's unclear whether that approach scales well. Sending an MCP tool command to click the button that says "X", is something a small local LLM can do. Writing complex code after parsing significant amount of HTML (for correct selectors for example) probably needs a managed model.
It's the wrong acronym. I wrote this blog post on the bike and used an LLM to fix up the dictation that I did. While I did edit it heavily and rewrote a lot of things, I did not end up noticing that my LLM expanded MCP incorrectly. It's Model Context Protocol.
Which I don't feel great about because I do not like to use LLMs for writing blog posts. I just really wanted to explore if I can write a blog post on my bike commute :)
I frankly use tools mostly as an auth layer for things were raw access is too big a footgun without a permissions step. So I give the agent the choice of asking for permission to do things via the shell, or going nuts without user-interaction via a tool that enforces reasonable limitations.
Otherwise you can e.g just give it a folder of preapproved scripts to run and explain usage in a prompt.
Not necessarily. I have a small little POC agentic tool on my side which is fully sandboxed, an it's inherently "non prompt injectable" by the data that it processes since it only ever passes that data through generated code.
Disclaimer: it does not work well enough. But I think it shows great promise.
Mashup Context Protocol, of course! There was a post the other day comparing MCP tools to the mashups of web 2.0. It’s a much better acronym expansion.
Microsoft Certified Professional, a very common certification.
Oh wait... hm ;) perhaps the writing nerds had it right when they recommend always writing the full acronym out the first time it's used in an article, no matter how common one presumes it to be
Unpopular Opinion: I hate Bash. Hate it. And hate the ecosystem of Unix CLIs that are from the 80s and have the most obtuse, inscrutable APIs ever designed. Also this ecosystem doesn’t work on Windows — which, as a game dev, is my primary environment. And no, WSL does not count.
I don’t think the world needs yet another shell scripting language. They’re all pretty mediocre at best. But maybe this is an opportunity to do something interesting.
Python environment is a clusterfuck. Which UV is rapidly bringing into something somewhat sane. Python isn’t the ultimate language. But I’d definitely be more interested in “replace yourself with a UV Python script” over “replace yourself with a shell script”. Would be nice to see use this as an opportunity to do better than Bash.
I realize this is unpopular. But unpopular doesn’t mean wrong.
Python CAN be a "shell script" in this case though...
Tool composition over stdio will get you very very far. That's what an interface "from the 80s" does for you 45 years later. That same stdio composability is easily pipe into/through any number of cli tools written in any number of languages, compiled and interpreted.
Composing via stdio is so bloody terrible. Layers and layers of bullshit string parsing and encoding and decoding. Soooo many bugs. And utterly undebuggable. A truly miserable experience.
Me, too. Also, Unix as a whole is overrated. One reason it won was an agreement mediated by a Federal judge presiding over an anti-trust trial that AT&T would not enter the computer market while IBM would not enter the telecommunications market, so Unix was distributed at zero cost rather than sold.
Want to get me talking reverentially about the pioneers of our industry? Talk to me about Doug Engelbart, Xerox PARC and the Macintosh team at Apple. There was some brilliant work!
> Also, Unix as a whole is overrated. One reason it won was an agreement mediated by a Federal judge presiding over an anti-trust trial that AT&T would not enter the computer market while IBM would not enter the telecommunications market, so Unix was distributed at zero cost rather than sold.
I see your point, but bear with me here--it kind of is.
I suppose if one wanted to be pedantically literal, then you are indeed correct. In every other meaningful consideration, the parent comment is. Maybe not Bash specifically, but #!/bin/sh is broadly available on nearly every connected device on the planet, in some capacity. From the perspective of how we could automate nearly anything, you'd be hard-pressed to find something more universal than a shell script.
tl;dr of one of today’s AI posts: all you need is code generation
It’s 2025 and this is the epitome of progress.
On the positive side code generation can be solid if you also have/can generate easy-to-read validation or tests for the generated code. I mean that you can read, of course.
In this analogy, do you have to design, construct, and learn from first principles to operate literally every other tool you'd like to use in addition to the saw?
Directionally I think this is right. Most LLM usage at scale tends to be filling the gaps between two hardened interfaces. The reliability comes not from the LLM inference and generation but the interfaces themselves only allowing certain configuration to work with them.
LLM output is often coerced back into something more deterministic such as types, or DB primary keys. The value of the LLM is determined by how well your existing code and tools model the data, logic, and actions of your domain.
In some ways I view LLMs today a bit like 3D printers, both in terms of hype and in terms of utility. They excel at quickly connecting parts similar to rapid prototyping with 3d printing parts. For reliability and scale you want either the LLM or an engineer to replace the printed/inferred connector with something durable and deterministic (metal/code) that is cheap and fast to run at scale.
Additionally, there was a minute during the 3D printer Gardner hype cycle where there were notions that we would all just print substantial amounts of consumer goods when the reality is the high utility use case are much more narrow. There is a corollary here to LLM usage. While LLMs are extremely useful we cannot rely on LLMs to generate or infer our entire operational reality or even engage meaningfully with it without some sort of pre-existing digital modeling as an anchor.
Hype cycle for drones and VR was similar -- at the peak, you have people claiming drones will take over package delivery and everyone will spend their day in VR. Reality is that the applicability is more narrow.
That's the claim for AR, not VR, and you're just noticing how research and development cycles play out, you can draw comparisons to literally any technology cycle.
Good drones are very Chinese atm, as is casual consumer drone delivery. Americans might be more than a decade away even with concerted bipartisan war-like effort to boost domestic drone competency.
The reality is Chinese.
I mean both of these things are actually happening (drone deliveries and people spending a lot of time in VR), just at a much much smaller scale than it was hyped up to be.
Drones and VR require significant upfront hardware investment, which curbs adoption. On the other hand, adopting LLM-as-a-service has none of these costs, so no wonder so many companies are getting involved with it so quickly.
Right, but abstract costs are still costs to someone, so how far does that go before mass adoption turns into a mass liability for whomever is ultimately on the hook? It seems like there is this extremely risky wager that everyone is playing--that LLM's will find their "killer app" before the real costs of maintaining them becomes too much to bear. I don't think these kinds of bets often pay off. The opposite actually, I think every truly revolutionary technological advance in the contemporary timeframe has arisen out of its very obvious killer app(s), they were in a sense inevitable. Speculative tech--the blockchain being one of the more salient and frequently tapped examples--tends to work in pretty clear bubbles, in my estimation. I've not yet been convinced this one is any different, aside from the absurd scale at which it has been cynically sold as the biggest thing since Gutenberg, but while that makes it somewhat distinct, it's still a rather poor argument against it being a bubble.
A parallel outcome for LLMs sounds realistic to me.
If it’s not happening at the scale it was pitched, then it’s not happening.
Considering what we've been seeing in the Russia-Ukraine and Iran-Israel wars, drones are definitely happening at scale. For better or for worse, I expect worldwide production of drones to greatly expand over the coming years.
This makes no sense, just because something didn't become as big as the hypemen said it would doesn't make the inventions or users of those inventions disappear.
For something to be considered “happening” you can’t just have a handful of localized examples. It has to be happening at a large noticeable scale that even people unfamiliar with the tech are noticing. Then you can say it’s “happening”. Otherwise, it’s just smaller groups of people doing stuff.
You checked out drone warfare? It’s all the rage in every conflict at the moment. The hype around drones is not fake, and I’d compare it more to autonomous cars because regulation is the only reason you don’t see a million private drones flying around.
Yes, to an extent, but I would say that is an extension of artillery and long-range fire capabilities.
As is well known, AI is whatever hasn't been done yet.
Interesting take but too bearish on LLMs in my opinion.
LLMs have already found large-scale usage (deep research, translation) which makes them more ubiquitous today than 3D printers ever will or could have been.
What we call an LLM today (by which almost everyone means an autogressive language model from the Generative Pretrained Transformer family tree, and BERTs are still doing important eork, believe that) is actually an offshoot of neural machine translation.
This isn't (intentionally at least) mere HN pedantry: they really do act like translation tools in a bunch of observable ways.
And while they have recently crossed the threshold into "yeah, I'm always going to have a gptel buffer open now" territory at the extreme high end, their utility outside of the really specific, totally non-generalizing code lookup gizmo usecase remains a claim unsupported by robust profits.
There is a hole in the ground where something between 100 billion and a trillion dollars in the ground that so far has about 20B in revenue (not profit) going into it annually.
AI is going to be big (it was big ten years ago).
LLMs? Look more and more like the Metaverse every day as concerns the economics.
>LLMs? Look more and more like the Metaverse every day as concerns the economics.
ChatGPT has 800M+ weekly active users how is that comparable to the Metaverse in any way?
I said as concerns the economics. It's clearly more popular than the Oculus or whatever, but it's still a money bonfire and shows no signs of changing on that front.
> There is a hole in the ground where something between 100 billion and a trillion dollars in the ground that so far has about 20B in revenue (not profit) going into it annually.
This is a concern for me. I'm using claude-code daily and find it very useful, but I'm expecting the price to continue getting jacked up. I do want to support Anthropic, but they might eventually need to cross a price threshold where I bail. We'll see.
I expect at some point the more open models and tools will catch up when the expensive models like ChatGPT plateau (assuming they do plateau). Then we'll find out if these valuations measure up to reality.
Note to the Hypelords: It's not perfect. I need to read every change and intervene often enough. "Vibe coding" is nonsense as expected. It is definitely good though.
I'm just taking advantage and burning VCs' money on useful but not world-changing tools while I still can. We'll come out of it with consumer-level okay tools even if they don't reach the levels of Claude today, though.
As a thought-exercise -- assume models continue to improve, whereas "using claude-code daily" is something you choose to do because it's useful, but is not yet at the level of "absolute necessity, can't imagine work without it". What if it does become, that level of absolute necessity?
- Is your demand inelastic at that point, if having claude-code becomes effectively required, to sustain your livelihood? Does pricing continue to increase, until it's 1%/5%/20%/50% of your salary (because hey, what's the alternative? if you don't pay, then you won't keep up with other engineers and will just lose your job completely)?
- But if tools like claude-code become such a necessity, wouldn't enterprises be the ones paying? Maybe, but maybe like health-insurance in America (a uniquely dystopian thing), your employer may pay some portion of the premiums, but they'll also pass some costs to you as the employee... Tech salaries have been cushy for a while now, but we might be entering a "K-shaped" inflection point --> if you are an OpenAI elite researcher, then you might get a $100M+ offer from Meta; but if you are an average dev doing average enterprise CRUD, maybe your wages will be suppressed because the small cabal of LLM providers can raise prices and your company HAS to pay, which means you HAVE to bear the cost (or else what? you can quit and look for another job, but who's hiring?)
This is a pessimistic take of course (and vastly oversimplified / too cynical). A more positive outcome might be, that increasing quality of AI/LLM options leads to a democratization of talent, or a blossoming of "solo unicorns"... personally I have toyed with calling this, something like a "techno-Amish utopia", in the sense that Amish people believe in self-sufficiency and are not wholly-resistant to technology (it's actually quite clever, what sorts of technology they allow for themselves or not), so what if we could take that further?
If there was a version of that Amish-mentality of loosely-federated self-sufficient communities (they have newsletters! they travel to each other! but they largely feed themselves, build their own tools, fix their own fences, etc.!), where engineers + their chosen LLM partner could launch companies from home, manage their home automation / security tech, run a high-tech small farm, live off-grid from cheap solar, use excess electricity to Bitcoin mine if they choose to, etc.... maybe there is actually a libertarian world that can arise, where we are no longer as dependent on large institutions to marshal resources, deploy capital, scale production, etc., if some of those things are more in-reach for regular people in smaller communities, assisted by AI. This of course assumes that, the cabal of LLM model creators can be broken, that you don't need to pay for Claude if the cheaper open-source-ish Llama-like alternative is good enough
Vibe coding is nonsense, and its really kind of uncomfortable to realize that a bunch of people you had tons of respect for are either ignorant or dishonest/bought enough to say otherwise. There's a cold wind blowing and the bunker-building crowd, well let's just say I won't shed a tear.
You don't stock antibiotics and bullets in a survival compound because you think that's going to keep out a paperclip optimizer gone awry. You do that in the forlorn hope that when the guillotines come out that you'll be able to ride it out until the Nouveau Regime is in a negotiating mood. But they never are.
Without trying to take away from your assertion, I think it is worthwhile to mention that part of this phenomenon is the unavoidable matter of meatspace being expensive and dataspace being intangibly present everywhere.
[citation needed]
large scale usage in niche domains is still small scale overall.
And yet you didn't provide a single reference link! Every case of LLM usage that I've seen claimed about those things has been largely a lie -- guess you won't take the opportunity to be the first to present a real example. Just another rumor.
My reference is the daily usage of chatgpt around me (outside of tech circles).
I don’t want to sound like a hard-core LLM believer. I get your point and it’s fair.
I just wanted to point out that the current usage of chatgpt is a lot broader than that of 3D printers even at the peak hype of it.
Outside of tech circles it looks like NFTs: people following hype using tech they don't understand which will be popular until the downsides we're aware of that they are ignorant to have consequences, and then the market will reflect the shift in opinion.
No way.
Everybody under a certain age is using ChatGPT, where they were once using search and friendship/expertises. It’s the number 1 app in the App Store. Copilot use in the enterprise is so seamless, you just talk to PowerPoint or outlook and it formulated what you were supposed to make or write.
It’s not a fad, it is a paradigm change.
People don’t need to understand how it works for it to work.
Even if the most bearish predictions turn out to be correct, the comparison of LLMs to NFTs is a galaxy-spanning stretch.
NFTs are about as close to literally useless as it gets, and that was always obvious; 99% of the serious attention paid to them came from hustlers and speculators.
LLMs, for all their limitations, are already good at some things and useful in some ways. Even in the areas where they are (so far) too unreliable for serious use, they're not pure hype and bullshit; they're doing things that would have seemed like magic 10 years ago.
Not even remotely in the same universe; the difference is ChatGPT is actually having an impact, people are incorporating it day-to-day in a way that NFTs never stood much of a chance.
I see it differently: people are switching to chatgpt like they switched to google back in 2005 (from whatever alternative existed back then)
And I mean random people, not tech circles
It’s very different from NFTs in that respect
No, 3D printers are the backbone of modern physical prototyping. They're far more important to today's global economy than LLMs are, even if you don't have the vantage point to see it from your sector. That might change in the future, but snapping your fingers to wink LLMs out of existence would change essentially nothing about how the world works today; it would be a non-traumatic non-event. There just hasn't been time to integrate them into any essential processes.
> snapping your fingers to wink LLMs out of existence would change essentially nothing about how the world works today
One could have said the same thing about Google in 2006
No, not even close. By 2006 all sorts of load-bearing infrastructure was relying on Google (e.g. Gmail). Today LLMs are still on the edge of important systems, rather than underlying those systems.
Things like BERT are a load bearing structure in data science pipelines.
I assume there are massive number of LLM analysis pipelines out there.
I suppose it depends if you consider non determinist DS/ML pipelines "loadbearing" or not. Most are not using LLMs though.
3D parts regularly are used beyond prototyping though as tooling for a small company can be higher than just metal 3D parts. So I do somewhat agree but the loss of productivity in software prototyping would be a massive hit if LLMs vanished.
> Directionally I think this is right.
We have a term at work we use called, "directionally accurate", when it's not entirely accurate but headed in the right direction.
this is a really good take
Something I've realized about LLM tool use is that it means that if you can reduce a problem to something that can be solved by an LLM in a sandbox using tools in a loop, you can brute force that problem.
The job then becomes identifying those problems and figuring out how to configure a sandbox for them, what tools to provide and how to define the success criteria for the model.
That still takes significant skill and experience, but it's at a higher level than chewing through that problem using trial and error by hand.
My assembly Mandelbrot experiment was the thing that made this click for me: https://simonwillison.net/2025/Jul/2/mandelbrot-in-x86-assem...
> The job then becomes identifying those problems and figuring out how to configure a sandbox for them, what tools to provide, and how to define the success criteria for the model.
Your test case seems like a quintessential example where you're missing that last step.
Since it is unlikely that you understand the math behind fractals or x86 assembly (apologies if I'm wrong on this), your only means for verifying the accuracy of your solution is a superficial visual inspection, e.g. "Does it look like the Mandelbrot series?"
Ideally, your evaluation criteria would be expressed as a continuous function, but at the very least, it should take the form of a sufficiently diverse quantifiable set of discrete inputs and their expected outputs.
That’s super cool, I’m glad you shared this!
I’ve been thinking about using LLMs for brute forcing problems too.
Like LLMs kinda suck at typescript generics. They’re surprisingly bad at them. Probably because it’s easy to write generics that look correct, but are then screwy in many scenarios. Which is also why generics are hard for humans.
If you could have any LLM actually use TSC, it could run tests, make sure things are inferring correctly, etc. it could just keep trying until it works. I’m not sure this is a way to produce understandable or maintainable generics, but it would be pretty neat.
Also while typing this is realized that cursor can see typescript errors. All I need are some utility testing types, and I could have cursor write the tests and then brute force the problem!
If I ever actually do this I’ll update this comment lol
Giving LLMs the right context -- eg in the form of predefined "cognitive tools", as explored with a ton of rigor here^1 -- seems like the way forward, at least to this casual observer.
1. https://github.com/davidkimai/Context-Engineering/blob/main/...
(the repo is a WIP book, I've only scratched the surface but it seems pretty brilliant to me)
> LLM in a sandbox using tools in a loop, you can brute force that problem
Does this require using big models through their APIs and spending a lot of tokens?
Or can this be done either with local models (probably very slow), or with subscriptions like Claude Code with Pro (without hitting the rate/usage limits)?
I saw the Mandelbrot experiment, it was very cool, but still a rather small project, not really comparable to a complex/bigger/older code base for a platform used in production
The local models aren't quite good enough for this yet in my experience - the big hosted models (o3, Gemini 2.5, Claude 4) only just crossed the capability threshold for this to start working well.
I think it's possible we'll see a local model that can do this well within the next few months though - it needs good tool calling, not an encyclopedic knowledge of the world. Might be possible to fit that in a model that runs locally.
> it needs good tool calling, not an encyclopedic knowledge of the world
I wonder if there are any groups/companies out there building something like this
Would love to have models that only know 1 or 2 languages (eg. python + js), but are great at them and at tool calling. Definitely don't need my coding agent to know all of Wikipedia and translating between 10 different languages
Given 2 datasets:
1. A special code dataset 2. A bunch of "unrelated" books
My understanding is that the model trained on just the first will never beat the model trained on both. Bloomberg model is my favorite example of this.
If you can squirell away special data then that special data plus everything else will beat the any other models. But that's basically what openai, google, and anthropic are all currently doing.
Wasn't there a tool calling benchmark by docker guys which concluded qwen models are nearly as good as GPT? What is your experience about it?
Personally I am convinced JSON is a bad format for LLMs and code orchestration in python-ish DSL is the future. But local models are pretty bad at code gen too.
There's a fine-tune of Qwen3 4B called "Jan Nano" that I started playing with yesterday, which is basically just fine-tuned to be more inclined to look things up via web searches than to answer them "off the dome". It's not good-good, but it does seem to have a much lower effective hallucination rate than other models of its size.
It seems like maybe similar approaches could be used for coding tasks, especially with tool calls for reading man pages, info pages, running `tldr`, specifically consulting Stack Overflow, etc. Some of the recent small MoE models from Chinese companies are significantly smarter than models like Qwen 4B, but run about as quickly, so maybe on systems with high RAM or high unified memory, even with middling GPUs, they could be genuinely useful for coding if they are made to be avoid doing anything without tool use.
Makes sense.
I treat an LLM the same way I'd treat myself as it relates to context and goals when working with code.
"If I need to do __________ what do I need to know/see?"
I find that traditional tools, as per the OP, have become ever more powerful and useful in the age of LLMs (especially grep).
Furthermore, LLMs are quite good at working with shell tools and functionalities (heredoc, grep, sed, etc.).
I've been using a VM for a sandbox, just to make sure it won't delete my files if it goes insane.
With some host data directories mounted read only inside the VM.
This creates some friction though. Feels like a tool which runs the AI agent in a VM, but then copies it's output to the host machine after some checks would help, so that it would feel that you are running it natively on the host.
This is very easy to do with Docker. Not sure it you want the vm layer as an extra security boundary, but even so you can just specify the VM’s docker api endpoint to spawn processes and copy files in/out from shell scripts.
Have you tried giving the model a fresh checkout in a read-write volume?
Hmm, excellent idea, somehow I assumed that it would be able to do damage in a writable volume, but it wouldn't be able to exit it, it would be self-contained to that directory.
I have the feeling that's not really MCP specifically VS other ways, but it is pretty simply: at the current state of AI, to have a human in the loop is much better. LLMs are great at certain tasks but they often get trapped into local minima, if you do the back and forth via the web interface of LLMs, ask it to write a program, look at it, provide hints to improve it, test it, ..., you get much better results and you don't cut yourself out to find a 10k lines of code mess that could be 400 lines of clear code. That's the current state of affairs, but of course many will try very hard to replace programmers that is currently not possible. What it is possible is to accelerate the work of a programmer several times (but they must be good both at programming and LLM usage), or take a smart person that has a relatively low skill in some technology, and thanks to LLM make this person productive in this field without the long training otherwise needed. And many other things. But "agentic coding" right now does not work well. This will change, but right now the real gain is to use the LLM as a colleague.
It is not MCP: it is autonomous agents that don't get feedbacks from smart humans.
So I run my own business (product), I code everything, and I use claude-code. I also wear all the other hats and so I'd be happy to let Claude handle all of the coding if / when it can. I can confirm we're certainly not there yet.
It's definitely useful, but you have to read everything. I'm working in a type-safe functional compiled language too. I'd be scared to try this flow in a less "correctness enforced" language.
That being said, I do find that it works well. It's not living up to the hype, but most of that hype was obvious nonsense. It continues to surprise me with it's grasp on concepts and is definitely saving me some time, and more importantly making some larger tasks more approachable since I can split my time better.
> try completing a GitHub task with the GitHub MCP, then repeat it with the gh CLI tool. You'll almost certainly find the latter uses context far more efficiently and you get to your intended results quicker.
This is spot on. I have a "devops" folder with a CLAUDE.md with bash commands for common tasks (e.g. find prod / staging logs with this integration ID).
When I complete a novel task (e.g. count all the rows that were synced from stripe to duckdb) I tell Claude to update CLAUDE.md with the example. The next time I ask a similar question, Claude one-shots it.
This is the first few lines of the CLAUDE.md
I feel like I'm taking crazy pills sometimes. You have a file with a set of snippets and you prefer to ask the AI to hopefully run them instead of just running it yourself?
The commands aren't the special sauce, it's the analytical capabilities of the LLM to view the outputs of all those commands and correlate data or whatever. You could accomplish the same by prefilling a gigantic context window with all the logs but when the commands are presented ahead of time the LLM can "decide" which one to run based on what it needs to do.
the snippets are examples. You can ask hundreds of variations of similar, but different, complex questions and the LLM can adjust the example for that need.
I don't have a snippet for, "find all 500's for the meltano service for duckdb syntax errors", but it'd easily nail that given the existing examples.
but if I know enough about the service to write examples, most of the time I will know the command I want, which is less typing, faster, costs less, and doesn't waste a ton of electricity.
In the other cases I see what the computer outputs, LEARN, and then the functionality of finding what I need just isn't useful next time. Next time I just type the command.
I don't get it.
LLMs are really good at processing vague descriptions of problems and offering a solution that's reasonably close to the mark. They can be a great guide for unfamiliar tools.
For example, I have a pretty good grasp of regular expressions because I'm an old Perl programmer, but I find processing json using `jq` utterly baffling. LLMs are great at coming up with useful examples, and sometimes they'll even get it perfect the first time. I've learned more about properly using `jq` with the help of LLMs than I ever did on my own. Same goes for `ffmpeg`.
LLMs are not a substitute for learning. When used properly, they're an enhancement to learning.
Likewise, never mind the idiot CEOs of failing companies looking forward to laying off half their workforce and replacing them with AI. When properly used, AI is a tool to help people become more productive, not replace human understanding.
Yes. I'm not the poster but I do something similar.
Because now the capabilities of the model grow over time. And I can ask questions that involve a handful of those snippets. When we get to something new that requires some doing, it becomes another snippet.
I can offload everything I used to know about an API and never have to think about it again.
Just as a related aside, you could literally make that bottom section into a super simple stdio MCP Server and attach that to Claude Code. Each of your operations could be a tool and have a well-defined schema for parameters. Then you are giving the LLM a more structured and defined way to access your custom commands. I'm pretty positive there are even pre-made MCP Servers that are designed for just this activity.
Edit: First result when looking for such an MCP Server: https://github.com/inercia/MCPShell
wouldn't this defeat the point? Claude Code already has access to the terminal, adding specific instruction in the context is enough
No. You are giving textual instructions to Claude in the hopes that it correctly generates a shell command for you vs giving it a tool definition with a clearly defined schema for parameters and your MCP Server is, presumably, enforcing adherence to those parameters BEFORE it hits your shell. You would be helping Claude in this case as you're giving a clearer set of constraints on operation.
Well, with MCP you’re giving textual instructions to Claude in hopes that it correctly generates a tool call for you. It’s not like tool calls have access to some secret deterministic mode of the LLM; it’s still just text.
To an LLM there’s not much difference between the list of sample commands above and the list of tool commands it would get from an MCP server. JSON and GNU-style args are very similar in structure. And presumably the command is enforcing constraints even better than the MCP server would.
Either way it is text instructions used to call a function (via a JSON object for MCP or a shell command for scripts). What works better depends on how the model you’re using was post trained and where in the prompt that info gets injected.
I do something similar, but the problem is that claude.md keeps on growing.
To tackle this, I converted a custom prompt into an application, but there is an interesting trade-off. The application is deterministic. It cannot deal with unknown situations. In contrast to CC, which is way slower, but can try alternative ways of dealing with an unknown situation.
I ended up with adding an instruction to the custom command to run the application and fix the application code (TDD) if there is a problem. Self healing software… who ever thought
I use a similar file, but just for myself (I've never used an LLM "agent"). I live in Emacs, but this is the only thing I use org-mode for: it lets me fold/unfold the sections, and I can press C-c C-c over any of the code snippets to execute it. Some of them are shell code, some of them are Emacs Lisp code which generates shell code, etc.
My absolute favorite use of MCP so far is Bruce Hauman's clojure-mcp. In short, it gives the LLM (a) a bash tool, (b) a persistent Clojure REPL, and (c) structural editing tools.
The effect is that it's far more efficient at editing Clojure code than any purely string-diff-based approach, and if you write a good test suite it can rapidly iterate back and forth just editing files, reloading them, and then re-running the test suite at the REPL -- just like I would. It's pretty incredible to watch.
I was just going to comment about clojure-mcp!! It’s far and away the coolest use of mcp I’ve seen so far.
It can straight up debug your code, eval individual expressions, document return types of functions. It’s amazing.
It actually makes me think that languages with strong REPLs are a better for languages than those without. Seeing clojure-mcp do its thing is the most impressive AI feat I’ve seen since I saw GPT-3 in action for the first time
The problem I see with MCP is very simple. It's using JSON as the format and that's nowhere as expressive as a programming language.
Consider a python function signature
list_containers(show_stopped: bool = False, name_pattern: Optional[str] = None, sort: Literal["size", "name", "started_at"] = "name"). It doesn't even need docs
Now convert this to JSON schema which is 4x larger input already.
And when generating output, the LLM will generate almost 2x more tokens too, because JSON. Easier to get confused.
And consider that the flow of calling python functions and using their output to call other tools etc... is seen 1000x more times in their fine tuning data, whereas JSON tool calling flows are rare and practically only exist in instruction tuning phase. Then I am sure instruction tuning also contains even more complex code examples where model has to execute complex logic.
Then theres the whole issue of composition. To my knowledge there's no way LLM can do this in one response.
How is JSON tool calling going to solve this?the reason to use the llm is that you dont know ahead of time that the vehicle type is only a car or motorcycle, and the llm will also figure out a way to detail bycicles and boats and airplanes, and to consider both left and right shoes separately.
the llm cant just be given this function because its specialized to just the two options.
you could have it do a feedback loop of rewriting the python script after running it, but whats the savings at tha point? youre wasting tokens talking about cars in python when you already know is a ski, and the llm could ask directly for the ski details without writing a script to do it in between
Great point.
But "the" problem with MCP? IMVHO (Very humble, non-expert) the half-baked or missing security aspects are more fundamental. I'd love to hear updates about that from ppl who know what they're talking about.
Regarding the Playwright example: I had the same experience this week attempting to build an agent first by using the Playwright MCP server, realizing it was slow, token-inefficient, and flaky, and rewriting with direct Playwright calls.
MCP servers might be fun to get an idea for what's possible, and good for one-off mashups, but API calls are generally more efficient and stable, when you know what you want.
Here's the agent I ended up writing: https://github.com/pamelafox/personal-linkedin-agent
Demo: https://www.youtube.com/live/ue8D7Hi4nGs
> It demands too much context.
This is solved trivially by having default initial prompts. All majors tools like Claude Code or Gemini CLI have ways to set them up.
> You pass all your tools to an LLM and ask it to filter it down based on the task at hand. So far, there hasn't been much better approaches proposed.
Why is a "better" approach needed? If modern LLMs can properly figure it out? It's not like LLMs don't keep getting better with larger and larger context length. I never had a problem with an LLM struggling to use the appropriate MCP function on it's own.
> But you run into three problems: cost, speed, and general reliability
- cost: They keep getting cheaper and cheaper. It's ridiculously inexpensive for what those tools provide.
- speed: That seem extremely short sighted. No one is sitting idle looking at Claude Code in their terminal. And you can have more than one working on unrelated topics. That defeats the purpose. No matter how long it takes the time spent is purely bonus. You don't have to spend time in the loop when asking well defined tasks.
- reliability: Seem very prompt correlated ATM. I guess some people don't know what to ask which is the main issue.
Having LLMS being able to complete tedious tasks involving so many external tools at once is simply amazing thanks to MCP. Anecdotal but just today it did a task flawlessly involving: Notion pages, Linear Ticket, git, GitHub PR, GitHub CI logs. Being in the loop was just submitting one review on the PR. All the while I was busy doing something else. And for what, ~1$?
> This is solved trivially by having default initial prompts. All majors tools like Claude Code or Gemini CLI have ways to set them up.
That only makes it worse. The MCP tools available all add to the initial context. The more tools, the more of the context is populated by MCP tool definitions.
Do you mean that some tools (MCP clients) pass all functions of all configured MCP servers in the initial prompt?
If that's the case: I understand the knee-jerk reaction but if it works? Also what theoretically prevents altering the prompt chaining logic in these tools to only expose a condensed list of MCP servers, not their whole capabilities, and only inject details based on LLM outputs? Doesn't seem like an insurmountable problem.
> Do you mean that some tools (MCP clients) pass all functions of all configured MCP servers in the initial prompt?
Not just some, all. That's just how MCP works.
> If that's the case: I understand the knee-jerk reaction but if it works?
I would not be writing about this if it worked well. The data indicates that it worse significantly worse than not using MCP because of the context rot, and the low too utilization.
I guess I don't see the technical limitation. Seem like a protocol update issue.
> cost: They keep getting cheaper and cheaper
no they don't[0], the cost is just still hidden from you but the freebies will end just like MoviePass and cheap Ubers
https://bsky.app/profile/edzitron.com/post/3lsw4vatg3k2b
"Cursor released a $200-a-month subscription then made their $20-a-month subscription worse (worse output, slower) - yet it seems even on Max they're rate limiting people!"
https://bsky.app/profile/edzitron.com/post/3lsw3zwgw4c2h
Fair. I'm using Claude Code which is pay as you go. The Market will probably do its things. (The company pays anyway obviously)
The cost will stay hidden from me because my job will pay it, just like the cost of my laptop, o365 license, and every other tool I use at work.
Until they use your salary to pay for another dozen licenses.
More appropriately: the terminal is all you need.
I have used MCP daily for a few months. I'm now down to a single MCP server: terminal (iTerm2). I have OpenAPI specs on hand if I ever need to provide them, but honestly shell commands and curl get you pretty damn far.
I never knew how far it was possible to go in bash shell with the built-in tools until I saw the LLMs use them.
I think the GitHub CLI example isn't entirely fair to MCP. Yes, GitHub's CLI is extensively documented online, so of course LLMs will excel at generating code for well-known tools. But MCP shines in different scenarios.
Consider internal company tools or niche APIs with minimal online documentation. Sure, you could dump all the documentation into context for code generation, but that often requires more context than interacting with an MCP tool. More importantly, generated code for unfamiliar APIs is prone to errors so you'd need robust testing and retry mechanisms built in to the process.
With MCP, if the tools are properly designed and receive correct inputs, they work reliably. The LLM doesn't need to figure out API intricacies, authentication flows, or handle edge cases - that's already handled by the MCP server.
So I agree MCP for GitHub is probably overkill but there are many legitimate use cases where pre-built MCP tools make more sense than asking an LLM to reverse-engineer poorly documented or proprietary systems from scratch.
> Sure, you could dump all the documentation into context for code generation, but that often requires more context than interacting with an MCP tool.
MCP works exactly that way: you dump documentation into the context. That's how the LLM knows how to call your tool. Even for custom stuff I noticed that giving the LLM things to work with that it knows (eg: python, javascript, bash) beats it using MCP tool calling, and in some ways it wastes less context.
YMMV, but I found the limit of tools available to be <15 with sonnet4. That's a super low amount. Basically the official playwright MCP alone is enough to fully exhaust your available tool space.
Ive never used that many. The LLM performances collapse/degrade significantly because of too much initial context? It seems like MCP implems updates could easily solve that. Like only injecting relevant servers for the given task based on initial user prompt.
That's handled by the MCP server in the sense of it doesn't do authentication, etc. it provides a simplified view of the world.
If that's what you wanted you could have designed that as your poorly documented internal API differently to begin with. There's zero advantage to MCP in the scenario you describe aside from convincing people that their original API is too hard to use.
Give it an objective and the LLM writes its own code and uses the tool iteratively to accomplish the task (as long as you can interact with it via a REST API).
For well known APIs, it does a fine job generating REST API calls.
You can pretty much do anything with this.
https://github.com/CharlieDigital/runjs
Yup, I can't help but think that a lot of the bad thinking comes from trying to avoid the following fact: LLMs are only good where your output does not need to be precise and/or verifiably "perfect," which is kind of the opposite of how code has worked, or has tried to work, in the past.
Right now I got it for: DRAFTS of prose things -- and the only real killer in my opinion, autotagging thousands of old bookmarks. But again, that's just to have cool stuff to go back and peruse, not something that must be correc.t
Wouldn't the sweet spot for MCP be where the LLM is able to do most of the heavy lifting on its own (outputting some kind of structured or unstructured output), but needs a bit of external/dynamic data that it can't do without? The list of MCP servers/tools it can use should nail that external lookup in a (mostly) deterministic way.
This would work best if a human is the end consumer of this output, or will receive manual vetting eventually. I'm not sure I'd leave such a system running unsupervised in production ("the Automation at Scale" part mentioned by the OP).
You don't solve the problem of being able to rely on the agent to call the MCP.
Hooks into the agent's execution lifecycle seem more reliable for deterministic behavior and supervision.
I agree. In any large backend software running on a server, it's the LLM invocation which would be a call out to an external system, and with proper validation around the results. At which point, calling an "MCP Server" is also just your backend software invoking one more library/service based on inspecting some part of the response from the LLM.
This doesn't take away from the utility of MCP when it comes Claude Desktop and the likes!
This is similar to the tool call (fixed code & dynamic params) vs code generation (dynamic code & dynamic params) discussion: tools offer contrains and save tokens, code gives you flexibility. Some papers suggest that generating code is often superior and this will likely become even more true as language models improve
[1] https://huggingface.co/papers/2402.01030
[2] https://huggingface.co/papers/2401.00812
[3] https://huggingface.co/papers/2411.01747
I am working on a model that goes a step beyond and even makes the distinction between thinking and code execution unnecessary (it is all computation in the end), unfortunately no link to share yet
You can combine MCPs within composable LLM generated code if you put in a little work. At Continual (https://continual.ai), we have many workflows that require bulk actions, e.g. iterating over all issues, files, customers, etc. We inject MCP tools into a sandboxed code interpreter and have the agent generate both direct MCP tool calls and composable scripts that leverage MCP tools depending on the task complexity. After a bunch of work it actually works quite well. We are also experimenting with continual learning via a Voyager like approach where the LLM can save tool scripts for future use, allowing lifelong learning for repeated workflows.
That autocompounding aspect of constantly refining initial prompts with more and more knowledge is so interesting. Gut feeling says it’s something that will be “standardized” in some way, exactly like what MCP did.
Yes, I think you could get quite far with a few tools like memory/todo list + code interpreter + script save/load. You could probably get a lot farther though if you RLVRed this similar to how o3 uses web search so effectively during it's thinking process.
I always dreamed of a tool which would know the intent, semantic and constraints of all inputs and outputs of any piece of code and thus could combine these code pieces automatically. It was always a fuzzy idea in my head, but this piece now made it a bit more clear. While LLMs could generate those adapters between distinct pieces automatically, it's a expensive (latency, tokens) process. Having a system with which not only to type the variables, but also to type the types (intents, semantic meaning, etc.) would be helpful but likely not sufficient. There has been so much work on ontologies, semantic networks, logical inference, etc. but all of it is spread all over the place. I'd like to have something like this integrated into a programming language and see what it feels like.
makes sense and if realized then deno is in excellent position to be one of the leading if not the main sandbox runtime for agents
We’re playing an endless cat and mouse game of capabilities between old and new right now.
Claude Code shows that the models can excel at using “old” programmatic interfaces (CLIs) to do Real Work™.
MCP is a way to dynamically provide “new” programmatic interfaces to the models.
At some point this will start to converge, or at least appear to do so, as the majority of tools a model needs will be in its pre-training set.
Then we’ll argue about MPPP (model pre-training protocol pipeline), and how to reduce knowledge pollution of all the LLM-generated tools we’re passing to the model.
Eventually we’ll publish the Merrium-Webster Model Tool Dictionary (MWMTD), surfacing all of the approved tools hidden in the pre-training set.
Then the kids will come up with Model Context Slang (MCS), in an attempt to use the models to dynamically choose unapproved tools, for much fun and enjoyment.
Ad infinitum.
MCP is literally the same as giving an LLM a set of man page summaries and a very limited shell over HTTP. It’s just in a different syntax (JSON instead of man macros and CLI args).
It would be better for MCP to deliver function definitions and let the LLM write little scripts in a simple language.
Anyone else switch their LLM subscription every month? I'm back on ChatGPT for O3 use, but expect that Grok4 will be next.
Tools are constraints and time/token savers. Code is expensive in terms of tokens and harder to constrain in environments that can’t be fully locked-down because network access for example is needed by the task. You need code AND tools.
> Code is expensive in terms of tokens and harder to constrain in environments
It's also true for human. But then we invented functions / libraries / modules
Honestly, I’m getting tired of these sweeping statements about what developers are supposed to be, how it’s “the right way to use AI”. We are in uncharted territories that are changing by the day. Maybe we have to drop the self-assurance and opinionated view points and tackle this like a scientific problem.
100% agreed - he mentions 3 barriers to using MCP over code: "cost, speed, and general reliability". But all 3 of these could change by 10-100x within a few years, if not months. Just recently OpenAI dropped the price of using o3 by 80%
This is not an environment where you can establish a durable manifesto
I think the Playwright MCP is a really good example of the overall problem that the author brings up.
However, I couldn’t really understand if he’s saying that the Playwright MCP is good to use for your own app or whether he means for your own app just tell the LLM directly to export Playwright code.
it's the latter: "you can actually start telling it to write a Playwright Python script instead and run that".
and while running the code might faster, it's unclear whether that approach scales well. Sending an MCP tool command to click the button that says "X", is something a small local LLM can do. Writing complex code after parsing significant amount of HTML (for correct selectors for example) probably needs a managed model.
> This is a significant advantage that an MCP (Multi-Component Pipeline) typically cannot offer
Oh god please no, we must stop this initialism. We've gone too far.
It's the wrong acronym. I wrote this blog post on the bike and used an LLM to fix up the dictation that I did. While I did edit it heavily and rewrote a lot of things, I did not end up noticing that my LLM expanded MCP incorrectly. It's Model Context Protocol.
And you shipped it to production. Just like real agentic coding! Nice!
Which I don't feel great about because I do not like to use LLMs for writing blog posts. I just really wanted to explore if I can write a blog post on my bike commute :)
We're all in line to get de-rezzed by the MCP, one way or another.
I would like to see MPC integrate the notion of hypermedia controls.
Seems like that would be a potential way to get self-organizing integrations.
Off topic: That font/layout/contrast on the page is very pleasing and inviting.
I frankly use tools mostly as an auth layer for things were raw access is too big a footgun without a permissions step. So I give the agent the choice of asking for permission to do things via the shell, or going nuts without user-interaction via a tool that enforces reasonable limitations.
Otherwise you can e.g just give it a folder of preapproved scripts to run and explain usage in a prompt.
I wonder if having 2 LLM's communicate will eventually be more like humans talking. With all the same problems.
I already have agents managing different repositories ask each other questions and make requests. It works pretty well for the most part.
The problem with this is that you have to give your LLM basically unbounded access to everything you have access to, which is a recipe for pain.
Not necessarily. I have a small little POC agentic tool on my side which is fully sandboxed, an it's inherently "non prompt injectable" by the data that it processes since it only ever passes that data through generated code.
Disclaimer: it does not work well enough. But I think it shows great promise.
what does "MCP" stand for?
Mashup Context Protocol, of course! There was a post the other day comparing MCP tools to the mashups of web 2.0. It’s a much better acronym expansion.
I didn't know either, but in the very first sentence, the author provides the expansion and a link to the Wikipedia page for it.
If you don't know what it is and can't be bothered to google it, then you probably aren't the audience for this.
I was about to say the same thing.
It’s bad writing practice to do this, even if you are assuming your followers are following you.
Especially for a site like Twitter that has a login wall.
I'm confused. It links to the definition in the first sentence, and I'm not sure what you mean by Twitter in this context.
Microsoft Certified Professional, a very common certification.
Oh wait... hm ;) perhaps the writing nerds had it right when they recommend always writing the full acronym out the first time it's used in an article, no matter how common one presumes it to be
Unpopular Opinion: I hate Bash. Hate it. And hate the ecosystem of Unix CLIs that are from the 80s and have the most obtuse, inscrutable APIs ever designed. Also this ecosystem doesn’t work on Windows — which, as a game dev, is my primary environment. And no, WSL does not count.
I don’t think the world needs yet another shell scripting language. They’re all pretty mediocre at best. But maybe this is an opportunity to do something interesting.
Python environment is a clusterfuck. Which UV is rapidly bringing into something somewhat sane. Python isn’t the ultimate language. But I’d definitely be more interested in “replace yourself with a UV Python script” over “replace yourself with a shell script”. Would be nice to see use this as an opportunity to do better than Bash.
I realize this is unpopular. But unpopular doesn’t mean wrong.
Python CAN be a "shell script" in this case though...
Tool composition over stdio will get you very very far. That's what an interface "from the 80s" does for you 45 years later. That same stdio composability is easily pipe into/through any number of cli tools written in any number of languages, compiled and interpreted.
Composing via stdio is so bloody terrible. Layers and layers of bullshit string parsing and encoding and decoding. Soooo many bugs. And utterly undebuggable. A truly miserable experience.
Me, too. Also, Unix as a whole is overrated. One reason it won was an agreement mediated by a Federal judge presiding over an anti-trust trial that AT&T would not enter the computer market while IBM would not enter the telecommunications market, so Unix was distributed at zero cost rather than sold.
Want to get me talking reverentially about the pioneers of our industry? Talk to me about Doug Engelbart, Xerox PARC and the Macintosh team at Apple. There was some brilliant work!
> Also, Unix as a whole is overrated. One reason it won was an agreement mediated by a Federal judge presiding over an anti-trust trial that AT&T would not enter the computer market while IBM would not enter the telecommunications market, so Unix was distributed at zero cost rather than sold.
What did Unix win?
Mind share of the basic design. Unix's design decisions are important parts of MacOS and Linux.
Multics would be an example of a more innovative OS than Unix, but its influence on the OSes we use today has been a lot less.
Nobody likes coding in bash but everyone does it (a little) because it is everywhere.
> because it is everywhere
Except for the fact that actually it is not everywhere.
I see your point, but bear with me here--it kind of is.
I suppose if one wanted to be pedantically literal, then you are indeed correct. In every other meaningful consideration, the parent comment is. Maybe not Bash specifically, but #!/bin/sh is broadly available on nearly every connected device on the planet, in some capacity. From the perspective of how we could automate nearly anything, you'd be hard-pressed to find something more universal than a shell script.
> you'd be hard-pressed to find something more universal than a shell script.
99.9% of my 20-year career has been spent on Windows. So bash scripts are entirely worthless and dead to me.
If you use git on Windows, bash is normally available. Agree, that this isn't widely used though.
tl;dr of one of today’s AI posts: all you need is code generation
It’s 2025 and this is the epitome of progress.
On the positive side code generation can be solid if you also have/can generate easy-to-read validation or tests for the generated code. I mean that you can read, of course.
Isn't it a bit like saying: saw is all you need (for carpenters)?
I mean, you _probably_ could make most furniture with only a saw, but why?
In this analogy, do you have to design, construct, and learn from first principles to operate literally every other tool you'd like to use in addition to the saw?