gary IH fung

There's a new kind of coding I call "vibe coding", where you fully give in to the vibes, embrace exponentials, and forget that the code even exists. It's possible because the LLMs (e.g. Cursor Composer w Sonnet) are getting too good. Also I just talk to Composer with SuperWhisper so I barely even touch the keyboard. I ask for the dumbest things like "decrease the padding on the sidebar by half" because I'm too lazy to find it. I "Accept All" always, I don't read the diffs anymore. When I get error messages I just copy paste them in with no comment, usually that fixes it. The code grows beyond my usual comprehension, I'd have to really read through it for a while. Sometimes the LLMs can't fix a bug so I just work around it or ask for random changes until it goes away. It's not too bad for throwaway weekend projects, but still quite amusing. I'm building a project or webapp, but it's not really coding - I just see stuff, say stuff, run stuff, and copy paste stuff, and it mostly works.

Okay my GPT-6.1 Terra benchmark with @VulcanBench is now finished 🖖 And before I talk about the key insights from this one, let me also remind everyone that VulcanBench is a coding benchmark. 100% of the tasks in my eval suite are coding tasks. Having now used Terra all week I can tell you, it's a stellar model for computer use, I'm crazy impressed. But VulcanBench isn't about seeing how good a model is at computer use, it's all about seeing how well a model completes more complex coding tasks. So now for analysis of GPT-6.1 Terra when it comes to coding. I typically say a score between 80 - 90 on Frontier v4 is the sweet spot for routine daily coding tasks. For GPT-6.1 Terra, this means that for coding, you'll want to use Extra High or Max only. Low, Medium, and High don't really make the cut. Looking at cost-per-task, it's a very inexpensive model to use, but when you look at it side-by-side with GPT-6.1 Sol, I'd probably just use Sol. For me, after looking at this data, I still plan to use GPT-6.1 Terra as my go-to for computer use, but I probably won't use it for coding. As usual, as a completely independent benchmark, everything I do at VulcanBench is available for you to review, inspect, and even run yourself if you want to. And the traces now include tool calls so you can really examine exactly how every model I benchmark thinks! Benchmark report: vulcanbench.com/benchmarks/s… Model page: vulcanbench.com/models/gpt-6… Public traces on GitHub: github.com/morganlinton/vulc… Live long and benchmark 🖖

Introducing agent-desktop agent-browser, for your whole computer → Computer Use for any coding agent → macOS, Windows and Linux → Runs in bg while you keep working → Agents use your computer in parallel, each with its own cursor

> be reddit > foster culture of rampant anti-capitalist woke circlejerking on the platform > advertisers can’t make $ from ads b/c reddit users are commies who think all businesses are evil > realize only way to profit is to license content to AI labs for training > AI labs already scraped all the content > require login to prevent future scraping > SEO traffic goes to zero > usage drops 90% > die a slow unremarkable death

Compaction is a mistake. When an agent's context fills up, tools like Codex and Claude Code compact it: 1. The model prefills the entire session again 2. It writes a summary of everything so far 3. A new session starts from that summary I measured this on a real ~400k-token session with Opus 5.5: → 38 s and $1.99 when the prompt cache has expired → 29 s and $0.17 when it hasn't And the summary is lossy. The exact error message, the file path, the one number you needed is often gone. So I do something new for Mainplane Context retrieval works the other way around. Mainplane keeps the full record of every session as a file. When the context fills up, a new session starts right away with one instruction: "You are continuing session X. Retrieve key context from it as you go." The agent searches the old session and copies the exact lines it needs, when it needs them. Same session, same model: 15 s and $0.10 to get back to work. It does not depend on a cache hit, and nothing is paraphrased. This is the bitter lesson applied to agent memory. Don't hand-design what the model should remember. Give it the full record and good tools, and let it decide. Mainplane is open source (MIT). Link in the first comment.

Why do I strongly suspect that this is just a prelude to the 20x becoming 10x? With the 5-hour usage limit going away as "bread and circuses" to distract people from what's coming down the pike.

Introducing Droid 1.0: the new engine behind Factory’s agent. Built for complex projects and everyday development: >more accurate >more efficient >faster than ever before

No. We've got to stop applying old human-centric patterns to working in the new era. delete all your wikis. have an agent answer questions from source instead. at spawn we have: - zero dashboard tools - zero docs - we do not make or use summaries everything we do is primary sources + intelligence Nothing to go stale Nothing to organize Nothing to navigate Always exactly up to date Perfectly personalized The answers get better with every model If we want to see a chart, we ask for it, and we have it in a minute If we want an answer to a question, we ask it, and we have a to-the-minute correct answer

“One AI lab basically eats the whole US economy” My brother in Christ, the danger of Google doing that created OpenAI, the danger of OpenAI doing it created Anthropic, the danger of Anthropic doing it created Meta Muse and SpaceX Grok, the danger of SpaceX doing it created Jeff Bezos Prometheus-Blue Origin.m… Have some goddamn faith in human greed dammit

Lots of debate this week on whether journals should ban AI in peer review. We actually tested this in our new preprint, and the results were pretty clear to me. For context, I've reviewed for most of the big journals and evaluated 1,000 human reviews as an editor. I love to read and evaluate science. TLDR: From now on I won't do any peer review without AI. For clinical trials, I'd even call human-only review dangerous 🧵

Day 25/ We are doubling all usage limits for all Pro 100/200/500 tiers and Business Premium, effective today. We heard your kind feedback and won’t be reducing usage limits for Pro 200, so your Work and Codex usage will stay the same. We’re also going to continue offering extended limits for dots for now, and usage of your dot will still continue to be separate from your default usage. And we'll of course also keep shipping better models and other efficiency improvements that increase your effective usage over time.

As someone who has personally spent $500k / mo+ on Google Ads for years, I can tell you with certainty: This revenue growth in Search is artificial & extremely unhealthy for Google’s business long term Search volumes are declining as legacy search is being increasingly cannibalized by non-monetized LLM queries Google’s response? Manufacture revenue growth via short-sighted, highly extractive, customer-hostile tactics. I.e. charge advertisers more for lower quality clicks, including clicks they do not want and explicitly did not approve Google to charge them for A few examples to illustrate: For all of its history until recently, Google operated on a 2nd price auction model I.e. if you bid $5 CPC and the next highest bidder bids $1 CPC, Google charged you $1.01 for the click (one penny more than the 2nd highest bidder) rather than the $5 you bid This was a genius move by Google early on as it incentivizes advertisers to input their true maximum willingness to pay rather than trying to play the game of bidding low and constantly adjusting to try to stay just ahead of the next highest bidder while still not paying too much However recently, Google silently deprecated the 2nd price auction and began charging advertisers as much as their bid and budget caps allow, regardless of what anyone else is bidding It’s a short-sighted cash grab at the expense of the long term health of the advertiser ecosystem Making thing worse, Google also recently nerfed keyword targeting precision Google previously had precise keyword targeting settings that allowed advertisers pick individual search phrases to bid on, defined down to the character w/ exact match or phrase match targeting This was one of the core features that made search advertising magic, enabling advertisers to run extremely precise campaigns based on exactly what their target customer typed But now, even if you bid on a specific term or phrase using the strictest exact -match targeting settings, Google will show your ad across 1000’s of unrelated keywords, labeling them as as “exact match (close variant)” The definition of “close variant” means whatever they want it to and changes constantly. The result is advertisers get billed for clicks that are totally irrelevant to their business and that their targeting settings explicitly forbid Google from targeting. Google does it anyway and there’s no ability to turn this off So now exact match is broad match, and broad match is just meaningless spam This is all very bad for advertisers, but for Google, it allows them to show your ad and bill you for clicks across 1000x more searches that were previously going unmonetized (mainly because they’re garbage queries no one wants) This is how you grow revenue atop declining search volumes Lastly, and perhaps most egregiously, Google quietly stopped respecting budget caps by a factor of 2x. For example campaigns we’ve been running for years with $1000 daily budget caps suddenly began spending $2000+ per day And the extra spend is entirely on the garbage keywords Google arbitrarily throws in as “exact match (close variants)” which have no value to our business, but can’t be turned off Google offers no refunds nor any recourse for overspend or spend on keywords you explicitly did not target These are not the actions of a healthy business. These are the actions of company whose core business is in decline but desperately needs to pump quarterly earnings so Wall Street will continue to fund insane capex while hopefully looking through their rapidly deteriorating negative free cash flow Google operated a benevolent monopoly for the better part of 25 yrs Meaning the value Google captured from Search was but a small fraction of the value it created, and that spread produced a potential energy that justified expectations of high earnings growth far, far into the future This is now no longer the case At the alter of AI capex, Google is sacrificing the golden goose

very weird thing happening rn technical people are sad, they think their skills are now useless non-technical people are desperately wishing they were more technical so they could use agents better

Today, we're shipping real-time graphics. Using Sol 6.1 Ultrafast we can generate custom overlays, diagrams, and even interactive 3D scenes right on a user's screen, no chat. It's a new visual interface for AI. Here's me learning about LLMs + hardware. Demo:

🚨BREAKING: The Liberals just voted against Bill C-264, leaving their Liberal tanker ban in place and preventing the export of crude oil from Prince Rupert on B.C's north coast. This shows that Prime Minister Mark Carney is not serious about making Canada an energy superpower.

Whether V4.1-1017 is at 19 or at 28 points on a new private eval is not just a question of "how many months/tiers behind". It's about the condition of internal research programs of different labs. Here we see that DS program is doing well. and xAI is underrated. and OpenAI.

1/ Excited to announce our new company Particle This is our first step in a series of models focused on developers. With particle-1, we focused on token efficiency and managed to keep parity in performance while using ~45% fewer tokens. Live now: particle.ai