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Patrick C Toulme
@PatrickToulme
gemini & TPUs @GoogleDeepmind. Formerly worked on Meta MTIA and AWS Trainium. Opinions are my own. All blogs shared are personal work.
445 Following    19.2K Followers
I used to be set on terminal UI for coding agents. @t3dotcodes convinced me that web UI is 1000x better than TUI. I do not even use my TUI anymore. Dynamic visualization is now a tool I can’t live without.
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The US midterm election day is tomorrow. Most people know the top of the ticket but almost nothing below it. Use the ballot-peek Grok @bot to quickly help you find all the candidates, their key positions and much more before you vote. Takes a minute.
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Always great to see new evals where the highest score is less than 15%. Also, cool to see the jump from Argon to Carbon and how cost effective the Gemini models are for their level of performance. Excited to see how this leaderboard looks like in a couple months given the rate of progress.
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The narrative is changing before your eyes
Official apology to Google from me, they cooked with Carbon. It's spikier in terms of domains and capabilities, but it's really good in a few areas.
Everyone I talk to (especially at non tech companies) tells me they hate using @MSFTCopilot compared to native harnesses like Claude Code, Codex and Antigravity. why do companies keep forcing their employees to use Copilot? and why does Microsoft not address all the customer feedback for Copilot? really confusing.
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Very soon I’ll post a 10-SWE year project. Note 10-SWE year is equivalent to 10 SWEs working for 1 year together
My take: you most likely are not thinking large enough with coding agents. Think much bigger. Next time you are using a coding agent ask yourself “what is a version of this task that would take years for a team of humans to do?” For example - instead of saying “Make this kernel faster” Try “Optimize my entire system and come back to me in 7 days” With recent coding models I have accomplished 50 SWE-year projects in single digit weeks.
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My take: you most likely are not thinking large enough with coding agents. Think much bigger. Next time you are using a coding agent ask yourself “what is a version of this task that would take years for a team of humans to do?” For example - instead of saying “Make this kernel faster” Try “Optimize my entire system and come back to me in 7 days” With recent coding models I have accomplished 50 SWE-year projects in single digit weeks.
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This is 100% true. My litmus test: Red flag when the main flex is: - how many agents (or teams of agents) they are running - how they can message their agent from the gym - how many millions of tokens they burned this week I see tons of these posts on LinkedIn daily. Legit when the main flex is: - they built something that wasn’t possible or economical before (ideally it makes or saves money) - they shipped more, or faster - they built custom software instead of using a SaaS provider IMO, the former is the equivalent of bragging about 80 hour work weeks with nothing to show for it.
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Increased competition in the AI coding market makes this possible. They wouldn’t allow this if competition was not fiercer than ever
This is 100% true. My litmus test: Red flag when the main flex is: - how many agents (or teams of agents) they are running - how they can message their agent from the gym - how many millions of tokens they burned this week I see tons of these posts on LinkedIn daily. Legit when the main flex is: - they built something that wasn’t possible or economical before (ideally it makes or saves money) - they shipped more, or faster - they built custom software instead of using a SaaS provider IMO, the former is the equivalent of bragging about 80 hour work weeks with nothing to show for it.
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I’m building multiplayer AI for going out no launch vid but i threw a banger launch party in bushwick with some niche artists and DJs instead Moves is on the app store starting now
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Starting to see “AI will eat software” predictions slowly coming true. My barber just moved his booking system off booksy after 5 years because a friend of his learned about Claude and vibe coded nearly the same thing for his website in a weakened. He told me it makes no sense to pay booksy anymore. I think this trend might accelerate starting from low margin and mom-and-pop businesses then spreading.
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There’s no moat in software anymore and do not let anyone tell u otherwise. All software can be cloned now
Starting to see “AI will eat software” predictions slowly coming true. My barber just moved his booking system off booksy after 5 years because a friend of his learned about Claude and vibe coded nearly the same thing for his website in a weakened. He told me it makes no sense to pay booksy anymore. I think this trend might accelerate starting from low margin and mom-and-pop businesses then spreading.
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One thing I learned from posting about buying RL data. There are literally 1 million companies in SF trying to sell RL data
my unfiltered thoughts on the data industry right now: there are too many brokers in the data space. in the last 4 months this industry has become the new equivalent to dropshipping. i have no issue with that. but if your entire moat is spam calling smb owners all day and begging them to license their data for a micro1 referral fee, you are not going to make it, and you are not the next mercor. most of you are not building anything at all. to everyone reading this, and the hundreds of other posts on your timeline from 18 year old sf kids claiming $200m run rates please ignore them. these are the modern day elizabeth holmes’. they're taking one day of contracted (not paid) revenue and multiplying it by 365. the worst i've seen is someone who made $100k off a micro1 referral fee in 6 hours and claimed they're at a $73m run rate (12 (daily working hours) / 6 x $100k x 365). this is literal fraud, especially if you raise money off of this claim. you see this in a lot of yc companies in recent batches. the only businesses i see surviving this are the giants that already exist (mercor, micro1, surge ai, turing) and the non consensus bets: consumer data collection apps like kled, robotics data collection companies like build ai and meckaai, etc. tldr: don’t believe everything you read, and just brokering company data is not a venture scale business and should not be claimed as such.
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Super excited for Gemini Carbon! I think the numbers speak for itself :)
We can finally talk you folks about a nonmetallic chemical element with the symbol C and atomic number 6. Hello Gemini 4 Carbon! I can't wait for this to be broadly available.
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Gemini 4 Carbon pushes the frontier of AI.
Gemini 4 Carbon is our next frontier model. It’s state-of-the-art across cybersecurity and long-horizon capabilities.
I’m curious to see how this handles PDFs. PDFs seem to be a technology that we all universally hate yet we are all forced to use. Using OCR to make editable PDFs would be a simple yet valuable use case for AI.
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Your OCR says it’s done—but did it get everything right? 🤔 In high-stakes workflows, even a small error can be costly. 🚀 Meet the MiniCPM-OCR suite: an open-source solution to parse fast and guard the results. Two new models: 🔹 MiniCPM-OCR 1.0 — SOTA OCR performance and speed at 1B scale. Compact, accurate, and fast. 🔹 MiniCPM-OCR Jev — A 2B decision model that helps assess parsing accuracy, with performance approaching that of Qwen3.5-397B-A17B on JevOCRBench. ⚡ What’s new? Small model, SOTA results: MiniCPM-OCR 1.0 scores 93.68 on Real5-OmniDocBench and 54.7 on OCRBench v2. A sharp goalkeeper 🛡️: MiniCPM-OCR Jev is model-agnostic, checking text, formulas, and tables from different OCR parsers. It achieves 93.6 on JevOCRBench, outperforming JEV-35B-A3B (90.1) and approaching Qwen3.5-397B-A17B (93.8). Up to 128 pages per minute 🤯: With DSpark, MiniCPM-OCR runs at 1.49× the speed of PaddleOCR-VL-1.6 and 3.62× that of MinerU2.5 Pro. The SDK’s concurrency optimizations further boost throughput to as much as 128 pages per minute. Deploy where you work: Day-0 support for Arm, Houmo AI, Intel, and NVIDIA, plus 10 more chip vendors through FlagOS. Ready for your stack: vLLM, SGLang, llama.cpp, and Ollama, with quantized models, fine-tuning support, and Agent Skills. 🔗 GitHub: 🤗 Hugging Face: MiniCPM-OCR 1.0: MiniCPM-OCR Jev: 👉 Try the Demo: Use the API:
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Any startups out there selling really good RL envs in kernels/compilers/chips (my personal domain of interest)? Or any cool RL envs in general? DM me.
I was at an AI dinner last night in NYC. Some interesting topics discussed 1. Performance engineers now spend most of their time managing agents and RSI loops. Most perf opts at major inference providers (together, fireworks) are found by agents. 2. RLing too much on code and math has negative effects on your model especially it’s language. 3. People were impressed by @bot and the overall acquisition of @cursor_ai by @spacexai. xAI is a legitimate frontier lab right now. 4. Most people have agents in loop writing, reviewing, and submitting PRs 24/7 5. Most agreed code review is not super productive anymore 6. Consensus was Fable 5.5 will be stunning but OpenAI will beat it in December with Bel
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