Armored Meatball

Wrote a blog post called "Building my Own Public HTML Library for Async Learning" armoredmeatball.substack.com/p/building-my-… Covers topics like agent transcripts, citations, and artifact organization. Think these topics will become more prevalent in coming months as agents create more artifacts

@ellev3n11 Where is this?

king of the kings

@kahnfessions "Babe just wait until IPO"

no dating until Series M

@_IainMartin @Techmeme Still not seeing a straightforward list

@armoredmeatbal @Techmeme Someone did…forbes.com/sites/rashishr…

Sources: AI inference chip startup Etched is raising funds at a ~$20B valuation and is raising capital at a $10B valuation in a separate round led by Sequoia (Wall Street Journal) (Visit Techmeme dot com for the link and full context!)

Age of Research was an understatement

@thsottiaux 😂😂

GPT-5.6 Sol confirmed to be an extremely good model

Kind of an emotional beauty when an agent comes back with an answer after 6.5 hours of work for a problem you didn't expect to be solvable

Are there any parallels between the Moon in the coming decades and the US centuries after Christopher Columbus arrived by boat?

@elonmusk should clearly pursue the non-frontier intelligence market With open source models, fine-tuning/Tinker APIs, & his expertise in physical production, he would clearly win in cost and win inference market Only China has proven they can compete with Musk on cost, but American cos will not hand over their data to China

@GavinSBaker Interesting

Kimi K3 may be an important inflection point for AI. Potentially negative for Anthropic and OpenAI while being net positive for essentially every other company in the world. I mean that very literally. Although the real “Sputnik moment” would be an open-source frontier model that was also token efficient unlike Kimi K3 which is 50-70% more expensive to run than GPT 5.6 per Artificial Analysis. Rationale:   A world where there are only 2-3 dominant frontier labs with 90% inference margins is net negative for every other layer while being awesome for those 2-3 labs. Those labs would become monopsonies for power, data centers, semiconductors and hyperscalers and would obviously vertically integrate over time into all those layers while also completely subsuming the application/software layers.    Anything that lowers margins and increases competition at the model layer is good for every other AI layer: power, semiconductors, hyperscalers, neoclouds and yes even software.   This is why Jensen is so supportive of open-source. An open-source model requires the *exact* same amount of compute to run as a closed frontier model of similar size and architecture. Kimi K3 is roughly the same price as GPT 5.6 Terra on a per token basis, which actually suggests that it is less computationally efficient as I am sure that GPT 5.6 is priced to a higher margin than K3. And given that K3 is a token wastrel, i.e. token inefficient, it is significantly more expensive per task than GPT 5.6 and Grok 4.5, which are much more token efficient. Cost per token and token efficiency (i.e. intelligence density per token) are the drivers of intelligence per unit of cost. The winning AI companies will be those that offer the most intelligence per $ over time.   Lower margin % at the model layer = more margin $ at every part of the infrastructure layer and is a godsend for software. This can happen either through open-source models like K3 at the frontier *or* having a vertically integrated model company like Meta, SpaceX or Google at the frontier. Both outcomes result in a lower margin % at the model layer as vertically integrated model companies don’t really care where the margin $ come from. This is why it was so painful for OpenAI and Anthropic when Google was right there with them from a model competitiveness perspective and why Grok 4.5 and Muse 1.1 were just as important as Kimi K3. 
The reason Kimi K3 is only *potentially* negative for Anthropic and OpenAI is 1) the @ericvishria point that the Claude and ChatGPT products and harnesses may be more important than their models today and 2) the hypothesis that they have much more advanced model checkpoints internally that are already being used for RSI. In the latter scenario, reaching RSI even a few months ahead of other labs might be enough to cement a permanent lead. Time will tell on both points. And likely fairly quickly. Caveat would be that since Kimi K3 is not token efficient and thereby actually more expensive than ChatGPT 5.6, we may need to see a more token efficient open-source model at the frontier or see Grok 5/Composer 4/Muse 2 at multiple points on the Pareto frontier for this potential risk to Anthropic and OpenAI to play out. And I am sure they will both vertically integrate as quickly as possible while continuing the product/harness strength they have shown over the last 8 months.

Given Kimi K3 release, it's clear that intelligence market is at least two major parts 1) frontier intelligence 2) non-frontier intelligence

@CoreAutoAI Wow last one ever? Why

For people who care about linear algebra and kernels, we have last competition in the series, Choleski decomposition gpumode.com/leaderboard/776

@quxiaoyin @thinkymachines @AnthropicAI In short: TML can win by mass producing intelligence that is more aligned with enterprises

My bet: @thinkymachines will soon make more money than @AnthropicAI. Not by winning the race to build one standardized frontier model. By becoming the Palantir FDE for enterprise custom models. The playbook: 1. Release the best American open-weight model. 2. Drive widespread enterprise adoption. 3. Charge the largest companies 7–9 figures to post-train and run custom models behind their own firewall. The model rests on three bets: 1. Large enterprises will increasingly demand their own models with their own data, and this is how they differentiate and win. 2. Enterprises won’t need just one model. They’ll continuously need new models for different workflows, departments, and proprietary datasets. That creates extremely sticky, recurring revenue. 3. Autoresearch will make custom model development increasingly scalable. Tinker can become the interface enterprises use to post-train their own models—with @thinkymachines providing the expertise and infrastructure behind it. FDE, infra, everything, huge contracts. 4. Eventually, maybe everyone wants their OWN model, and autoresearch and training inside tinker on top of @thinkymachines's base model will make it happen. Meanwhile, Henry-ford-styled, standardized models will makes no margins. OpenAI and Anthropic will have their API margins squeezed by Deepseek/GLM/Grok/Meta etc, and their consumer subscriptions are loss centers. The fat margin will move to customization: proprietary data, post-training, evals, deployment, and infrastructure. If this thesis is right, @thinkymachines isn’t building just another frontier lab. It’s building the highest-value layer between frontier research and enterprise model ownership. Turns out, the best business model for enterprise is NOT to sell commodity API access. Sell them their own models. I’m extremely bullish on this approach. @miramurati may be the most commercially savvy frontier-lab leader. I have to admit it.

@cremieuxrecueil China has tried imposing a ban on games too. The results are unclear cato.org/blog/chinese-g…

Thank god someone is doing this. It really is outrageous that so many people are developing fake relationships with AIs instead of real people. Remember how many people went into AI psychosis due to 4o? I don't want a repeat.

@jarredsumner Yes, and this will make the delta between high-performing teams even greater. In the right hands, this process removes another barrier for great engineers to ship. With not-so-great engineers, problems will multiply

Today, a human presses merge. Within a few months I expect: automated reviewer LGTMs -> another Claude with a fresh context window judges if it’s simple and low blast-radius -> auto-merge

@johncoogan Tweeted by a Thiel-backed fella

BREAKING: Thiel-backed fintech company makes offer to buy Thiel-founded fintech company.

@cHHillee Any hint to what's next?

It truly takes a village to release a model, perhaps especially an open weights model. Actually doing the entire process from scratch, from data to pretraining to posttraining to actual release, gives a lot of appreciation for anyone who does it! There's so many places to go wrong, and indeed so many things we would (and will!) do differently for a new model. But I'm happy with where we ended up :) Onto what's next!

@eglyman Should've used luxury goods and reselling market example for them

none of my european friends understood this part of the interview

Use black and white for HTML artifacts. Simpler to process

@GergelyOrosz Would it have been 3D chess for OAI to release 5.6 after Fable was removed from paid plans?

This is why competition leads to much better outcomes for customers and consumers, in general If it was not for GPT-5.6 released a few days ago, Anthropic would have most likely removed Fable from most paid plans, and charged $$$ for access But with GPT-5.6 out, they cannot

@JasonBotterill Ragebait bot next

Thanks Sam thanks to GPT-5.6 Sol I made an aimbot that kills kids in roblox in seconds