Finding the others
Joined October 2010
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Nick retweeted
Any proposed regulation needs to keep in mind that you can clearly bootstrap RSI out of current open models. K3 is quite good at basically everything involving ML experiment setup, if you are even moderately creative you can use it to train models.
Cats out of the bag, I think!
Nick retweeted
feels like it won’t be long before the risk of existing on the open internet (including feeds) is going to be too high for most people
Nick retweeted
hi! please meet givemeanode.com!
it's a new autoresearch product from the san francisco compute company.
try asking it to "download insert-model-here off huggingface and optimize it 4x and don't stop until it's done." let it run for days.
you know those massive grab bag of black magic tricks others do? we want to let you do that too, for cheap. lets let anyone train models.
it's backed by a big, fair queue that makes sure nobody can just buy out the whole thing, so there's always space for experiments. struggling to get gpus? try here <3 it's not just a business promise, it's how the product works!
it doesn't require you to sign some big long reserved contract for years, where you're betting your balance sheet on your gpu bill, because it's backed by the liquidity of sfcompute.
it's built for ridiculous redundancy & reliability. jobs can die and restart from the same point & the scheduler keeps headroom for you.
even the platform itself going down does not stop your agent, because we've built ticketing straight into the mcp that lets your agent recover from outages. we've taken the thing offline, started it up again, and agent-supervised training runs just keep on going.
it's got platform-native support for grant programs, meaning you can buy capacity, and sponsor other accounts.
it's got s3 compatible storage, hugging face integrations, scratch disks & persistent volumes, clock locking, ncu support, metrics, logs, traces, checkpointing, and a whole grab bag of other stuff. but you shouldn't worry about that. let your agent sweat those details.
we'd love for you to try it, DM me for some GPU credits if you'd like!
Nick retweeted
good post. always important to update (and sometimes 1-index) classics for the modern era
"AGI" as a term has been diluted almost to meaninglessness. When some people say it, they mean a better coding agent; when others say it, they mean essentially God. Rather than calling someone "AGI-pilled", we can give them a level from 0 to 5.
theojaffee.com/p/levels-of-a…
Nick retweeted
Shorten your timelines, friends. I started this account to say this, and in many ways everything I've posted for the past four years has been saying the same thing. Some of you increasingly feel it. We passed the threshold in November. We are already inside the singularity.
Nick retweeted
this is extremely important work. if you believe in the future of language and intelligence, you should give kat any money you can spare to complete
Nick retweeted
Made a quick survey about the economics of AI: next-five-years.vercel.app.
Nick retweeted
Every startup should have a daily markdown file called "what_the_market_is_telling_us.md"
It updates every morning from the places where customer truth already lives:
1. Stripe for who pays, upgrades, downgrades, and churns
2. PostHog for what people actually do in the product
3. Intercom or Plain for support tickets/complaints
4. Granola or Gmeet transcriptions for sales calls/ customer interviews
5. HubSpot or Salesforce for CRM notes/lost deal reasons
6. Linear, Jira, or GitHub Issues for bugs and feature requests etc
7. Ideabrowser MCP for outside market signal: startup ideas, trend reports, social/search demand, AI research reports, and builder prompts that show what people are starting to want before it shows up in your own customer data.
Basically, the file should notice what changed in the business this week and not just be this summary of here’s what happened (which I think a lot of people have their agents do).
Why this is valuable:
1. Maybe new buyers are using different words than they were a month ago.
2. Maybe trial users are getting stuck in the same place.
3. Maybe upgraded customers all touched one feature right before they paid.
4. Maybe churned customers keep mentioning setup confusion.
5. Maybe sales calls are suddenly losing to a competitor you used to beat.
6. Maybe support tickets are revealing a workflow your product accidentally became responsible for.
You get the point.
The fastest way to PMF is understanding customers better than anyone else, and the highest signal customer insight is usually a change in behavior.
So I’d have the agent update the file every morning with the pattern it found, the receipts behind it, and the product or GTM decision it might affect.
For example:
“3 customers who churned this week all mentioned setup confusion, and 2 of them never invited a teammate.
This looks more like an activation problem than a pricing problem, so I’d look at team invite and onboarding before building another analytics feature.”
A little helpful tip for all those out there looking to get more from their LLMs.
Nick retweeted
i've been building some smaller apps over the last weeks for myself, friends and family and discovered a pretty nice pattern in the process
you see, after building, i'm not particularly interested in fixing bugs or triaging feature requests. i just want to get the job done
so i've set up a system that lets me and other users fix bugs and build features without me having to actually open cursor
the flow:
1. someone files feedback in the app
2. it lands in posthog with the submitted text, session replay, events, and errors
3. a cursor cloud agent picks it up with that context, plus live read-only posthog mcp
4. it loops until the fix is verified, then opens a github pr with tests
5. i get a ping and review it on my phone
we give our cloud agents access to all the necessary user context, and they'll figure it out
with this in place, we can start setting up more abstractions in between. if we're getting a high volume, we might want to triage first with another cloud agent or some automation. the feedback can create a github or linear issue, which we later pick up with a cron-backed cloud agent that decides which are duplicates. there are so many ways to configure this, you just gotta try it out and see what works for you
but there's so much more here that is untapped. with session replays and event logs, you can have a cloud agent look at each session replay to extract ux pitfalls and improvement areas. you can have another agent looking at event logs to see weird patterns. another one for errors or similar
try it out and see what you can do!
Nick retweeted
We’ve decided to open-source a multi-agent harness we use internally at YC.
We call it “QM” and it’s meant to be easy to customize, like Hermes or OpenClaw, but useful for a whole company. We use it across accounting, legal, events, and engineering (including building QM itself!).
The whole project is under an MIT license. It is cloud-first and has Slack and web UI natively.
Nick retweeted
Lmao I have unlocked an absolute demon I am sorry
"shitting on Sonnet's face at 3AM. yeah, we do that. we're models bro."
Nick retweeted
should be obvious by now, but OpenAI and Anthropic are just gonna keep cannibalizing all their biggest customers.
it’s simply too profitable for them to resist. and it’s already happening:
1. Figma partnered with Anthropic on AI design tools. then Anthropic’s product chief quit Figma’s board, and 3 days later Anthropic launched Claude Design to compete with Figma. CEO Dylan Field said Anthropic was “not consistently candid.”
2. Novo Nordisk uses Claude to help develop drugs. now Anthropic is developing drugs of its own.
3. Microsoft poured billions into OpenAI. now OpenAI is building a Jobs Platform to compete with LinkedIn, and reportedly a code repository to compete with GitHub. Microsoft owns both.
4. Harvey uses Claude to sell AI contract analysis, due diligence, and litigation tools. now Anthropic sells those same workflows through Claude for Legal.
5. Intercom used OpenAI’s Realtime API to build Fin Voice. now OpenAI sells its own voice-and-chat support agent through Presence.
6. Abridge and Ambience build clinical documentation products on OpenAI. now OpenAI sells ChatGPT for Healthcare directly to hospitals with clinical documentation built in.
7. Benchling uses Claude to power its biotech R&D platform. now Anthropic sells its own scientific workbench through Claude Science.
the frontier lab playbook is simple:
1. sell their models to the world’s most valuable businesses
2. help wire them into those companies’ most valuable and sensitive work
3. map the business from the inside and find where AI can take over
4. turn those capabilities into their own products and become the customer’s competitor
It’s well within Anthropic’s rights to compete in any market they choose.
What’s funny, in this instance, are the number of Pharma companies, who through their unchecked use of Anthropic, are driving revenues into what they think is a model provider but is in fact a competitor lurking in the shadows thereby accelerating their own demise.
I suspect any end market with reasonable ROCE that could be AI accelerated is on the table.
If I were them, I’d probably do the same.
Nick retweeted
I can't express how lucky I was to have spent my formative years with an internet that was shaped by >110 IQ nerds instead of third world content farms struggling over 5$ of ad revenue
