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parsa
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parsa
@ParsaKhaz
on a mission to transform how humans interact with their work @dcoupleAI w/ https://t.co/PkuOk6U9ab + https://t.co/KepwLGwTgs | also: https://t.co/ifBsuOxEZN / https://t.co/IYYZ61D8ch / ex-AI2 Incubator
San Francisco, CA Katılım Ocak 2022
1.7K Takip Edilen738 Takipçiler
It’s already here @usedoozy
Alex Lieberman@businessbarista
Someone is going to build a worldclass “Brain” for enterprises & make a stupid amount of money. Why? As @da_fant said, “coding w ai is solved bc all context is in the git repo. knowledge work is difficult bc context is spread out. an ai system that creates a git repo w all context for a knowledge worker will be able to 100% automate the work.” When companies talk about being data ready for AI, this is what they’re implicitly saying. Engineering has been prepared for this moment for a long time because of the deterministic nature of code, the centralization/versioning of data (read: GitHub), and AI tools that are largely build by engineers for engineers. But for the rest of white collar work, there’s a TON of catching up to do to properly harness the power of the technology. The big challenge here, and why no one has truly cracked the code for "an ai system that creates a git repo w all context for a knowledge worker" is because unlike code, most knowledge is 1) distributed, 2) unstructured, and 3) unverifiable. It's distributed: transcripts live in Granola. Documents in Notion. Customer Data in Hubspot. ERP. Emails. Slack messages. Random spreadsheets. SOP docs. Etc. Etc. Building an ingestion engine that connects to all of your disparate data sources and auto-updates based on the shelf-life of the data is the first, and frankly, easiest step of the process. Next, it's unstructured: let's say I want to create a proposal for a potential client. To nail the proposal, I want it to pull important information from a variety of sources. The specific asks & background from our initial sales call. Previous proposals to anchor ourselves to a proven format. And completed sprint boards from Linear, so the pricing & timeline in the document is grounded in truth. Whether it's a thoughtful filesystem (a la Obsidian) or an OpenClaw-esque memory structure, the brain needs to be great at self-organizing in a thoughtful schema. This is very hard, especially if you want to build a generalizable brain that can be shaped to an array of different enterprises. And finally, most knowledge is unverifiable: writing a function, running a unit test, and seeing if the code works is easy. It works or it doesn't. Using AI to accelerate your content creation process is highly subjective. What is a good/bad idea? Is the content in your voice or not? Does it feel like slop or novel? Answering these questions are both difficult and non-verifiable. That same system described above doesn't just have to be great at organizing & forming coherent relationships, but it also has to be great at self-improving based on feedback from the user. Memory systems (like those introduced by OpenClaw) are great to a point, but as you scale the corpus of data within your company's brain, things like compaction and cleaning become wildly important to avoid the needle in the haystack problem. Someone is going to figure out how to solve this problem, and when they do, not only will they make a shit ton of money, but they'll be robinhood for knowledge workers, enabling non-engineers to enjoy the sort of leverage that only technical folks have felt for the last few years.
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@adamtowerz Yeah I’ve found around 250k it starts to degrade but w/ 200k that upper bound is closer to 120k which makes me hit it 2x faster yk?
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@ParsaKhaz Never use 1MM. Any context over ~150k is purely for extra room when compacting
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> downgrade 4.6 capabilities in weeks coming up to launch
> launch 4.7
> we are so back (to OG 4.6 capabilities)
I’d been using opus 4.5 200k context w/ my skill stack and getting better results then 4.6 1 mills degraded perf
Good to be back
Boris Cherny@bcherny
Opus 4.7 is in Claude Code today. It's more agentic, more precise, and a lot better at long-running work. It carries context across sessions and handles ambiguity much better.
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parsa retweetledi
Watching the Cowork wave flood into GTM and Ops teams in big companies and dear lord, it has never been more valuable to understand how software works.
Creative system thinkers are on a high. Folks without an intuition for software are building skills and plugins the wrong way - not able to make any edge cases work, building fragile, slow token-expensive solutions, not able to test their own skills..
My takeaway from this is that either:
1) AGI gets so good it will correct people and inform them of better approaches to achieve their goals or
2) There will always be a massive market for vertical focused agent platforms that abstract the underlying systems and best practices so people don't need to know how software works
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This is why you need model agnostic harnesses
Nicholas Larus-Stone@nlarusstone
Pair Opus with GPT5.4 and Gemini 3.1 and experience superintelligence
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i think one of the most impt recursive loops in startups is startups that improve marketing/revenue growth (aka GTM) because they can obviously dogfood it for themselves. considering starting a track for this at AIE WF due to its imptnce.
luma.com/04bg11l5
this is @UpsideGTM + @ExaAILabs's new AI GTM meetup - i'll be looking for speakers from this crew. apply if you want to be featured.
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@swyx You should totally do this. I’m running a AI Tinkerers event for GTM Engineering next month if you want to see what it’s like.
All the presenters are going to be engineers, and they can only present if 1) they built something themselves that generated revenue
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Had the opportunity to try this out last week with @hawaalidrammeh and doozy's capabilities blew my mind(and creeped a out a bit ngl). Congrats on launch Parsa!
parsa@ParsaKhaz
1,800 downloads in 3 weeks. my $120K/yr job now takes me 20 minutes a day. we spent 6 months in stealth building a to-do list that completes itself. today we're coming out. meet @usedoozy ↓
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