More to throw in the Big Deal pile openai.com/index/openai...
Who's Who of AI
Phillip Carter
Why they matter
Practitioner with public evidence across Compute & infrastructure.
- AI signals
- 6 past 30d
- Sources
- 6 distinct domains
- Discussions
- 43 past 30d
- Latest signal
- 14d ago
morbjs maintainer
work: salesforce, past @honeycomb.io and @microsoft.com
play: computers, backpacking, snowboarding, hiking, kayaking
What they're sharing
openai.com
Evaluating AGENTS.md: Are Repository-Level Context Files Helpful for Coding Agents? arxiv.org
CrankGPT — fully offline, human-powered local AI squeezlabs.github.io
The Shape of Things to Come, Part 1: The Continuous Thunderdome — Steve Yegge yegge.ai
Don't be a meat proxy gruhn.me
Do people click on links in Google AI summaries? | Pew Research Center pewresearch.org
Tracing the thoughts of a large language model anthropic.com
ads.openai.com
Agent swarms and the new model economics · Cursor cursor.com
AI in Search is driving more queries and higher quality clicks blog.google
From Storage to Experience: A Survey on the Evolution of LLM Agent Memory Mechanisms arxiv.org
openai.com
Articles & links
Well this reads as quite the combo breaker, at least with agents powered by LLMs ~3 months ago, developer-submitted and LLM-created AGENT md files don't seem to improve task performance for python projects, but do burn ~20% more tokens arxiv.org/abs/2602.11988
AI Weekly's analysis
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- Context files like AGENTS.md tend to reduce coding agent task success while raising inference cost by over 20% on average, the paper reports.
- On a new 138-issue Python benchmark, LLM-generated context files added 3.92 steps per task and pushed costs up by 23%.
- The finding held across Claude Sonnet-4.5, GPT-5.2, GPT-5.1 mini and Qwen3-30b-coder, so it is not tied to one model family.
Read full analysis →
Holy shit this is awesome, hand-cranked LLM inference squeezlabs.github.io/handcrank/
AI Weekly's analysis
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- CrankGPT runs a full voice-interactive AI pipeline on a Raspberry Pi 5 with 8GB RAM, powered solely by a 20W hand-crank generator.
- Cold-start to functional conversation takes roughly 30 seconds; time to first token ranges from 0.8 to 2.9 seconds depending on model size.
- Memory bandwidth, not raw compute, is the primary bottleneck for on-device LLM inference, with DDR5 hardware achieving 29-58% faster token generation than DDR4.
Read full analysis →
lmao gastown didn't fail, it was Claude that failed gastown yegge.ai/essays/the-s...
Ooohhhh I love this term, "meat proxy" gruhn.me/blog/2026-08...
Some early research definitely shows less click-throughs, but it doesn't say much about if the clicked link was a garbage site or not. In theory with wikipedia, .gov, reddit, etc. showing up more in sources there's more opportunities for better content I guess www.pewresearch.…
AI Weekly's analysis
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- Pew found Google users click a traditional search result in 8% of visits when an AI summary appears, versus 15% without one.
- Users clicked on a link inside the AI summary itself in just 1% of visits, per Pew's 68,879-search dataset.
- Browsing sessions ended on 26% of pages with an AI summary, compared with 16% of pages without one.
Read full analysis →
I also don’t buy the idea of “not reasoning”, mostly because I think such a concept takes many forms. But even then we’re finding that they’re internally planning ahead and not just spitting out the next word www.anthropic.com/research/tra...
I'm about 25% accurate on takes so my take is that this is going to be wildly successful for OpenAI ads.openai.com
AI Weekly's analysis
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- OpenAI opened its 'Advertise in ChatGPT' self-serve portal on July 22, 2026, with Best Buy, Lowe's and VistaPrint as named early advertisers.
- Sponsored blocks appear below ChatGPT responses and are served only to adult Free and Go plan users; Plus, Pro and Business remain ad-free.
- Targeting runs on conversational context signals rather than keywords, and OpenAI states ads do not influence ChatGPT's answers.
Read full analysis →
Cool post but we got a long way to go before we get (literally) religious dedication to specs fed to agents cursor.com/blog/agent-s...
AI Weekly's analysis
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Read full analysis →
I'm curious how this meshes with their own (generic) words, where they claim they're sending clicks to ostensibly better content than before. I don't know if I quite buy it, but there's also so many unreliable narrators around that it's hard to know for sure blog.google/produc…
Survey papers are so funny sometimes. Like this one about memory systems for LLMs that has to hamfist in some math expressions at the start and then never use them arxiv.org/abs/2605.06716
Huge news for openai openai.com/index/openai...
Their own posts
Recent commentary
job change out --> salesforce in <-- github Gonna help build some systems behind Copilot and see if we can make it way more capable than it is today. So much ground to cover for all coding agents, maybe Copilot will soon be The One now that claude has murky vibes
Oh not a whole lot of PMs in my follows but two bad things are currently surging in PM world: 1. Writing overly verbose specs/PRDs with AI, not even bothering to get creative with prompts 2. Using claude code to spit out a hifi gestalt of a UI prototype and dev teams getting pressured to ship
Still stunlocked that Marc Andreessen and his big brain prompts AI models with “don’t hallucinate”
My dumb AI take of the day is that I think it was wrong to introduce 1M+ context windows on account of coherence != capacity
What's the state of the art on coding agent memory these days? I haven't used it much, at least not with Claude. So far the best memory implementation I've seen is just normal ChatGPT. It seems to do retrieval quite well.
I hadn't realized how easy it is to get started with base llama.cpp these days. I gave up on ollama a while ago since they started getting weird. Anyways: brew install llama brew install pi pi install git:github.com/huggingface/pi-llama llama serve <model> pi Got muh gemma4 on muh macbook
I see ChatGPT has no sense of irony. Or maybe it has an incredible one?
It's nuts how much a modern codebase with good DevEx practices can move so fast with AI compared to ... not that. One of my teams now has daily (as opposed to weekly) turnaround times for most features with Claude. Another team's "velocity" is imperceptible compared to a year ago.
I think there’s something to it that Ed Zitron gets a lot of readers who love his overly verbose writing … and that a lot of people get impressed with the overly verbose words barfed out by LLMs in default settings These groups are nominally non-intersecting but I don’t buy it
I bet Terry A. Davis would be doing some pretty wild shit with LLMs if he were around today
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