I built something similar using an MCP that allows claude to "outsource" development to GLM 4.7 on Cerebras (or a different model, but GLM is what I use). The tool allows Claude to set the system prompt, instructions, specify the output file to write to and crucially allows it to list which additional files (or subsections of files) should be included as context for the prompt.

Ive had great success with it, and it rapidly speeds up development time at fairly minimal cost.

Maybe they set up an agent for it.

They asked Google to cover them this time. They will owe them a reciprocal favour.

> In my opinion, they solved the wrong problem

> I don't want a faster, smaller model. I want a faster, better model

Will you pay 10x the price? They didn't solve the "wrong problem". They did what they could with the resources they have.

> In my opinion, they solved the wrong problem. The main issue I have with Codex is that the best model is insanely slow, except at nights and weekends when Silicon Valley goes to bed. I don't want a faster, smaller model (already have that with GLM and MiniMax). I want a faster, better model (at least as fast as Opus).

It's entirely possible that this is the first step and that they will also do faster better models, too.

You're describing almost verbatim what we're building at Octigen [1]! Happy to provide a demo and/or give you free access to our alpha version already online.

[1] https://octigen.com

disagree. while intelligence is important, speed is especially important when productionizing AI. it’s difficult to formalize the increase in user experience per increase in TPS but it most definitely exists.

I doubt it; there's a limit on model size that can be supported by Cerebras tech. GPT-5.3 is supposedly +1T parameters...

I will always take more speed. My use of LLMs always comes back to doing something manually, from reviewing code to testing it to changing direction. The faster I can get the LLM part of the back-and-forth to complete, the more I can stay focused on my part.

These are the ones I look for every time a new model is released. Incorporates so many things into one single benchmark.

Also your blog is tops. Keep it up, love the work.

I mean they are only running a small version of codex can they run the full one? Or the technology isn't there yet?

I think it's a beta so they're trying to figure out pricing by deploying it.

Are they really thinking or are they sprinkling them with Sleep(x)?

update codex, it's there.

I love the probabilistic nature of this. Presentations could be anywhere from extremely impressive to hilariously embarrassing.

I disagree. This is great for bulk tasks: renaming, finding and searching for things, etc

You always want faster feedback. If not a human leveraging the fast cycles, another automated system (eg CI).

Slow, deep tasks are mostly for flashy one-shot demos that have little to no practical use in the real world.

I built something similar at a hackathon, a dynamic teleprompter that adjusts the speed of tele-prompting based on speaker tonality and spoken wpm. I can see extending the same to an improv mode. This is a super cool idea.

As an associate professor who spends a ridiculous amount of time preparing for lectures, I would love to try this in one of my courses

can we plese make the bluey bench the gold standard for all models always

I love the idea of a living slide deck. This feels like a product that needs to exist!

I read this headline and was like, "A look, an announcement by GPT!! That means that Google or Anthropic must have had a release today!"

And, yup, there is Gemini in item 3!

I wonder why they named it so similiarly to the normal codex model while it much worse, while cool of course.

It's "dinner-plate sized" because it's just a full silicon wafer. It's nice to see that wafer-scale integration is now being used for real work but it's been researched for decades.

yep

Can I just say how funny this metric is?

"Our model is so slow and our tokens/second is so low that these tasks can take hours!" is not the advertising they think it is.

PEBKAC

This might actually be hard for open source agents (e.g. Opencode) to replicate, barring a standardized WebSocket LLM API being widely adopted.

Lying with charts → https://handsondataviz.org/how-to-lie-with-charts.html

Also → https://medium.com/@hypsypops/axes-of-evil-how-to-lie-with-g...

More → https://researchguides.library.yorku.ca/datavisualization/li...

And → https://vdl.sci.utah.edu/blog/2023/04/17/misleading/

Yes, we want this.

Can you show one?

How do you handle the diagrams?

Can you compare it to Opus 4.6 with thinking disabled? It seems to have very impressive benchmark scores. Could also be pretty fast.

Nvidia seems cooked.

Google is crushing them on inference. By TPUv9, they could be 4x more energy efficient and cheaper overall (even if Nvidia cuts their margins from 75% to 40%).

Cerebras will be substantially better for agentic workflows in terms of speed.

And if you don't care as much about speed and only cost and energy, Google will still crush Nvidia.

And Nvidia won't be cheaper for training new models either. The vast majority of chips will be used for inference by 2028 instead of training anyway.

Nvidia has no manufacturing reliability story. Anyone can buy TSMC's output.

Power is the bottleneck in the US (and everywhere besides China). By TPUv9 - Google is projected to be 4x more energy efficient. It's a no-brainer who you're going with starting with TPUv8 when Google lets you run on-prem.

These are GW scale data centers. You can't just build 4 large-scale nuclear power plants in a year in the US (or anywhere, even China). You can't just build 4 GW solar farms in a year in the US to power your less efficient data center. Maybe you could in China (if the economics were on your side, but they aren't). You sure as hell can't do it anywhere else (maybe India).

What am I missing? I don't understand how Nvidia could've been so far ahead and just let every part of the market slip away.

Not for what they are using it for. It is $1m+/chip and they can fit 1 of them in a rack. Rack space in DC's is a premium asset. The density isn't there. AI models need tons of memory (this product annoucement is case in point) and they don't have it, nor do they have a way to get it since they are last in line at the fabs.

Their only chance is an aquihire, but nvidia just spent $20b on groq instead. Dead man walking.

Yet investors keep backing NVIDIA.

Just wish they weren't so insanely expensive...

How hard have you tried?

I've been finding that the Opus 4.5/4.6 and GPT-5.2/5.3 models really have represented a step-change in how good they are at running long tasks.

I can one-shot prompt all sorts of useful coding challenges now that previously I would have expected to need multiple follow-ups to fix mistakes the agents made.

I got all of this from a single prompt, for example: https://github.com/simonw/research/tree/main/cysqlite-wasm-w... - including this demo page: https://simonw.github.io/research/cysqlite-wasm-wheel/demo.h... - using this single prompt: https://github.com/simonw/research/pull/79

Cerebras is a bit of a stunt like "datacenters in spaaaaace".

Terrible yield: one defect can ruin a whole wafer instead of just a chip region. Poor perf./cost (see above). Difficult to program. Little space for RAM.

I routinely leave codex running for a few hours overnight to debug stuff

If you have a deterministic unit test that can reproduce the bug through your app front door, but you have no idea how the bug is actually happening, having a coding agent just grind through the slog of sticking debug prints everywhere, testing hypotheses, etc — it's an ideal usecase

Their ability to burn through tokens non-stop for hours, days or weeks without intervention.

It worked for me several times.

It's easy to say that these increasingly popular tools are only able to produce useless junk. You haven't tried, or you haven't "closed the loop" so that the agent can evaluate its own progress toward acceptance criteria, or you are monitoring incompetent feeds of other users.

The other day I got Codex to one-shot an upgrade to Vite 8 at my day job (a real website with revenue). It worked in this for over 3 hours without intervention (I went to sleep). This is now in production.

They were afraid for the untested tech but it looks like a leap in speed now

What codex often does for this, write a small python script and execute that to bulk rename for example.

I agree that there is use for fast "simpler" models, there are many tasks where the regular codex-5.3 is not necessary but I think it's rarely worth the extra friction of switching from regular 5.3 to 5.3-spark.

It would be so cool if it generated live in the presentation and adjusted live as you spoke, so you’d have to react to whatever popped on screen!

I mean, yes, one always does want faster feedback - cannot argue with that!

But some of the longer stuff - automating kernel fusion, etc, are just hard problems. And a small model - or even most bigger ones, will not get the direction right…

Added a thinking-disabled Opus 4.6 timing. It took 1m 4s – coincidentally the same as 5.3-codex-low.

The end result would be a normal PPT presentation, check https://sli.dev as an easy start, ask Codex/Claude/... to generate the slides using that framework with data from something.md. The interesting part here is generating these otherwise boring slide decks not with PowerPoint itself but with AI coding agents and a master slides, AGENTS.md context. I’ll be showing this to a small group (normally members only) at IPAI in Heilbronn, Germany on 03/03. If you’re in the area and would like to join, feel free to send me a message I will squeeze you in.

Man I hope someone drinks Nvidia's milk shake. They need to get humbled back to the point where they're desperate to sell gpus to consumers again.

Only major road block is cuda...

Well they `acquired` groq for a reason.

I believe they licensed smth from groq

At this point Tech investment and analysis is so divorced from any kind of reality that it's more akin to lemmings on the cliff than careful analysis of fundamentals

They claim the opposite, though, saying the chip is designed to tolerate many defects and work around them.

You’re mixing up Open AI for Anthropic.

Anthropic is actually sort of concerned with not burning through cash and charging people a reasonable price. Open AI doesn’t care. I can use Codex CLI all day and not approach any quotas with just my $20 a month ChatGPT subscription.

I treat coding agents like junior developers and never take my hand off the wheel except for boilerplate refactoring.

In my AGENTS.md file i have a _rule_ that tells the model to use Apache ECharts, the data comes from the prompt and normally .csv/.json files. Prompt would be like: "After slide 3 add a new content slide that shows a bar chart with data from @data/somefile.csv" ... works great and these charts can be even interactive.

> let every part of the market slip away.

Which part of the market has slept away, exactly ? Everything you wrote is supposition and extrapolation. Nvidia has a chokehold on the entire market. All other players still exist in the small pockets that Nvidia doesn’t have enough production capacity to serve. And their dev ecosystem is still so far ahead of anyone else. Which providers gets chosen to equip a 100k chips data center goes so far beyond the raw chip power.

> What am I missing?

Largest production capacity maybe?

Also, market demand will be so high that every player's chips will be sold out.

Oh don't worry. Ever since the power issue started developing rack space is no longer at a premium. Or at least, it's no longer the limiting factor. Power is.

The real question is what’s their perf/dollar vs nvidia?

The bigger the chip, the worse the yield.

What do you mean? The generated script just downloads the sources and runs pyodide: https://github.com/simonw/research/blob/main/cysqlite-wasm-w...

There is maybe 5 relevant lines in the script and nothing complex at all that would require to run for days.

Can you share any examples of these one-shot prompts? I've not gotten to the point where I can get those kind of results yet.

I have a hard time understanding how that would work — for me, I typically interface with coding agents through cursor. The flow is like this: ask it something -> it works for a min or two -> I have to verify and fix by asking it again; etc. until we're at a happy place with the code. How do you get it to stop from going down a bad path and never pulling itself out of it?

The important role for me, as a SWE, in the process, is verify that the code does what we actually want it to do. If you remove yourself from the process by letting it run on its own overnight, how does it know it's doing what you actually want it to do?

Or is it more like with your usecase—you can say "here's a failing test—do whatever you can to fix it and don't stop until you do". I could see that limited case working.

> it's an ideal usecase

This is impressive, you’ve completely mitigated the risk of learning or understanding.

I'm definitely bullish on LLM's for coding. It sounds to me as though getting it to run on its own for hours and produce something usable requires more careful thought and setup than just throwing a prompt at it and wishing for the best—but I haven't seen many examples in the wild yet

How can you afford that?

This is nonsense what do you mean? Mistral uses Cerebras for their LLMs as well. [0]

It's certainly not "untested".

[0] https://www.cerebras.ai/blog/mistral-le-chat

How did you verify it?

Some consulting firms do this, one guy is giving the presentation live while others are in the next meeting room still banging out the slides.

and with neuralink it would generate slides of the audience naked

From my experience, larger models also don't get the direction right a surprising amount of times. You just take more time to notice when it happens, or start to be defensive (over-specing) to account for the longer waits. Even the most simple task can appear "hard" with that over spec'd approach (like building a react app).

Iterating with a faster model is, from my perspective, the superior approach. Doesn't matter the task complexity, the quick feedback more than compensates for it.

The dirty secret is that there is plenty of power. But, it isn't all in one place and it is often stranded in DC's that can't do the density needed for AI compute.

Training models needs everything in one DC, inference doesn't.

Exactly. They won't ever tell you. It is never published.

Let's not forget that the CEO is an SEC felon who got caught trying to pull a fast one.

There was a pre-LLM version of this called "battledecks" or "PowerPoint Karaoke"[0] where a presenter is given a deck of slides they've never seen and have to present on it. With a group of good public speakers it can be loads of fun (and really impressive the degree that some people can pull it off!)

0. https://en.wikipedia.org/wiki/PowerPoint_karaoke

Every presentation becomes improv

> Nvidia has a chokehold on the entire market.

You're obviously not looking at expected forward orders for 2026 and 2027.

What about other ad hoc diagrams like systems architecture, roadmaps, mind maps, etc.

These are the bane of any staff engineers life - lol. Because people above need to know a plan in art form.

So seriously interested on how I can make it easier

> Largest production capacity maybe?

Anyone can buy TSMC's output...

I guess it depends what you mean by "perf". If you optimize everything for the absolutely lowest latency given your power budget, your throughput is going to suck - and vice versa. Throughput is ultimately what matters when everything about AI is so clearly power-constrained, latency is a distraction. So TPU-like custom chips are likely the better choice.

Or Google TPUs.

I suggest to read their website, they explain pretty well how they manage good yield. Though I’m not an expert in this field. I does make sense and I would be surprised if they were caught lying.

No, not for days - but it churned away on that one for about ten minutes.

I don't think I've got any examples of multi-hour or multi-day sessions that ran completely uninterrupted - this one back in December took 4.5 hours but I had to prompt it to keep going a few times along the way: https://simonwillison.net/2025/Dec/15/porting-justhtml/

If you look through the commit logs on simonw/research and simonw/tools on GitHub most commits should either list the prompt, link to a PR with the prompt or link to a session transcript.

Maybe so, but I did once spend 12 hours straight debugging an Emscripten C++ compiler bug! (After spending the first day of the jam setting up Emscripten, and the second day getting Raylib to compile in it. Had like an hour left to make the actual game, hahah.)

I am a bit thick with such things, but just wanted to provide the context that Emscripten can be a fickle beast :)

I sure am glad I can now deploy Infinite Mechanized Autistic Persistence to such soul-crushing tasks, and go make a sandwich or something.

(The bug turned out to be that if I included a boolean in a class member, the whole game crashed, but only the Emscripten version. Sad. Ended up switching back to JS, which you basically need anyway for most serious web game dev.)

I use Codex CLI or Claude Code

I don't even necessarily ask it to fix the bug — just identify the bug

Like if I've made a change that is causing some unit test to fail, it can just run off and figure out where I made an off-by-one error or whatever in my change.

For some reason setting up agents in a loop with a solid prompt and new context each iteration seems to result in higher quality work for larger or more difficult tasks than the chat interface. It's like the agent doesn't have to spend half its time trying to guess what you want

You do things like ralph loops.

https://github.com/snarktank/ralph

Its constantly restarting itself, looking at the current state of things, re-reading what was the request, what it did and failed at in the past (at a higher level), and trying again and again.

Or, they have freed up time for more useful endeavours, that may otherwise have spent on drudgery.

I don't discount the value of blood, sweat and tears spent on debugging those hard issues, and the lessons learned from doing so, but there is a certain point where it's OK to take a pass and just let the robots figure it out.

it's more like "this function is crashing with an inconsistent file format error. can you figure out how a file with the wrong format got this far into the pipeline?". in cases like that the fix is usually pretty easy once you have the one code path out of several thousands nailed down.

well, you can start with https://github.com/rcarmo/go-textile, https://github.com/rcarmo/go-rdp, https://github.com/rcarmo/go-ooxml, https://github.com/rcarmo/go-busybox (still WIP). All of these are essentially SPEC and test-driven and they are all working for me (save a couple of bugs in go-rdp I need to fix myself, and some gaps in the ECMA specs for go-ooxml that require me to provide actual manually created documents for further testing).

I am currently porting pyte to Go through a similar approach (feeding the LLM with a core SPEC and two VT100/VT220 test suites). It's chugging along quite nicely.

It costs $200 for a month

Just send it bro

(but honestly for a lot of websites and web apps you really can just send it, the stakes are very low for a lot of what most people do, if they're honest with themselves)

This comment doesn't make sense.

“here's a failing test—do whatever you can to fix it”

Bad idea. It can modify the code that the test passes but everything else is now broken.

It needs a closed loop.

Strategy -> [ Plan -> [Execute -> FastVerify -> SlowVerify] -> Benchmark -> Learn lessons] -> back to strategy for next big step.

Claude teams and a Ralph wiggum loop can do it - or really any reasonable agent. But usually it all falls apart on either brittle Verify or Benchmark steps. What is important is to learn positive lessons into a store that survives git resets, machine blowups, etc… Any telegram bot channel will do :)

The entire setup is usually a pain to set up - docker for verification, docker for benchmark, etc… Ability to run the thing quickly, ability for the loop itself to add things , ability to do this in worktree simultaneously for faster exploration - and got help you if you need hardware to do this - for example, such a loop is used to tune and custom-fuse CUDA kernels - which means a model evaluator, big box, etc….

Tested at Mistral’s scale is a very different thing to tested at OpenAI’s scale.

There is a Jackbox game called "Talking Points" that's like this: the players come up with random ideas for presentations, your "assistant" (one of the other players) picks what's on each slide while you present: https://www.youtube.com/watch?v=gKnprQpQONw

I had a butterfly take over my live DreamScape slide show demo at the 1995 WWDC.

https://youtu.be/5NytloOy7WM?t=321

I think most estimates have Nvidia at more or less stable share of CoWoS capacity (around 60%), which is ~doubling in '26.

Not my normal use-case, but you can always fall back and ask the AI coding agent to generate the diagram as SVG, for blocky but more complex content like your examples it will work well and still is 100% text based, so the AI coding agents or you manually can fix/adjust any issues. An image generation skill is a valid fallback, but in my opinion it's hard to change details (json style image creation prompts are possible but hard to do right) and you won't see changes nicely in the git history. In your use case you can ask the AI coding agent to run a script.js to get the newest dates for the project from a page/API, then it should only update the dates in the roadmap.svg file on slide x with the new data. This way you will automagically have the newest numbers and can track everything within git in one prompt. Save this as a rule in AGENTS.md and run this every month to update your slides with one prompt.

Claude code can output Excalidraw format files which can be imported directly into the webapp. You can MCP it too if you want.

You could try something like mermaid (or ASCII) -> nano banana. You can also go the other way and turn images into embedded diagrams (which can be interactive depending on how you're sharing the presentation)

Isn't that such a great outcome. No more robotic presentations. The best part is that you can now practice Improv at the comfort of your home.

TPUs don't have enough memory either, but they have really great interconnects, so they can build a nice high density cluster.

Compare the photos of a Cerebras deployment to a TPU deployment.

https://www.nextplatform.com/wp-content/uploads/2023/07/cere...

https://assets.bwbx.io/images/users/iqjWHBFdfxIU/iOLs2FEQxQv...

The difference is striking.

Can anyone buy TSMC though?

By perf I mean how much does it cost to serve 1T model to 1M users at 50 tokens/sec.

> Throughput is ultimately what matters

I disagree. Yes it does matter, but because the popular interface is via chat, streaming the results of inference feels better to the squishy messy gross human operating the chat, even if it ends up taking longer. You can give all the benchmark results you want, humans aren't robots. They aren't data driven, they have feelings, and they're going to go with what feels better. That isn't true for all uses, but time to first byte is ridiculously important for human-computer interaction.

I've heard this said a lot but never had this problem. Claude has been decent at debugging tests since 4.0 in my experience (and much better since 4.5)

You say this with such confidence and then ask if smaller chips require smaller wafers.

Sure it does. If it’s many small dies on a wafer, then imperfections don’t ruin the entire batch; you just bin those components. If the entire wafer is a single die, you have much less tolerance for errors.

One wafer will turn into multiple chips.

Defects are best measured on a per-wafer basis, not per-chip. So if if your chips are huge and you can only put 4 chips on a wafer, 1 defect can cut your yield by 25%. If they're smaller and you fit 100 chips on a wafer, then 1 defect on the wafer is only cutting yield by 1%. Of course, there's more to this when you start reading about "binning", fusing off cores, etc.

There's plenty of information out there about how CPU manufacturing works, why defects happen, and how they're handled. Suffice to say, the comment makes perfect sense.

Bigger chip = more surface area = higher chance for somewhere in the chip to have a manufacturing defect

Yields on silicon are great, but not perfect

The scale of being "tested" clearly convinced Meta (beyond OpenAI's scale) [0] HuggingFace [1], Perplexity [2] and unsuprisingly many others in the AI industry [3] that require more compute than GPUs can deliver.

So labelling it "untested" even at Meta's scale as a customer (which exceeds OpenAI's scale) is quiet nonsensical and frankly an uninformed take.

[0] https://www.cerebras.ai/customer-spotlights/meta

[1] https://www.cerebras.ai/news/hugging-face-partners-with-cere...

[2] https://www.cerebras.ai/press-release/cerebras-powers-perple...

[3] https://www.cerebras.ai/customer-spotlights

I do it easily just by asking Codex

And this product will work great for any industry... can I get a suggestion for an industry from the crowd?

Audience: Transportation... Education... Insurance...

Speaker: Great! I heard "Healthcare".

Right... as we can see from this slide, this product fits the "Healthcare" industry great because of ...

No. TSMC will not take the risk on allocating capacity to just anyone given the opportunity cost.

Oh wow the cabling in the first link is really sloppy!

You just have to change the "popular interface" to something else. Chat is OK for trivia or genuinely time-sensitive questions, everything else goes through via email or some sort of webmail-like interface where requests are submitted and replies come back asynchronously. (This is already how batch APIs work, but they only offer a 50% discount compared to interactive, which is not enough to really make a good case for them - especially not for agentic workloads.)

All 1T models are not equal. E.g. how many active parameters? what's the native quantization? how long is the max context? Also, it's quite likely that some smaller models in common use are even sub-1T. If your model is light enough, the lower throughput doesn't necessarily hurt you all that much and you can enjoy the lightning-fast speed.

Although, IIUC, Cerebras expects some amount of imperfection and can adjust the hardware (or maybe the software) to avoid those components after they're detected. https://www.cerebras.ai/blog/100x-defect-tolerance-how-cereb...

That's why you typically fuse off defective sub-units and just have a slightly slower chip. GPU and CPU manufacturers have done this for at least 15 years now, that I'm aware of.

You can just do dynamic binning.

Does that mean smaller chips are made from smaller wafers?

Caro’s first LBJ biography tells of how the future president became a congressman in Texas in his 20s, by carting around a “claque” of his friends to various stump speeches and having them ask him softball questions and applauding loudly after

Well, hey, who needs friends?

Just pick some reasonable values. Also, keep in mind that this hardware must still be useful 3 years from now. What’s going to happen to cerebras in 3 years? What about nvidia? Which one is a safer bet?

On the other hand, competition is good - nvidia can’t have the whole pie forever.

Nope. They use the same size wafers and then just put more chips on a wafer.

> Just pick some reasonable values.

And that's the point - what's "reasonable" depends on the hardware and is far from fixed. Some users here are saying that this model is "blazing fast" but a bit weaker than expected, and one might've guessed as much.

> On the other hand, competition is good - nvidia can’t have the whole pie forever.

Sure, but arguably the closest thing to competition for nVidia is TPUs and future custom ASICs that will likely save a lot on energy used per model inference, while not focusing all that much on being super fast.

So, does a wafer with a huge chip has more defects per area than a wafer with 100s of small chips?

AMD