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Joined October 2020
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Adithya Krishnan retweeted
recommended reading. seen many of his observations IRL myself.
for these tools to diffuse into the economy productively, we need to educate folks on a specific kind of thinking.
some individuals figure it out on their own and become very productive. super hard to scale.
ludic.mataroa.blog/blog/ai-m…
🤯
Big news: Kimi-K3 by @Kimi_Moonshot is now #1 in the Frontend Code Arena with 1679 pts, surpassing Claude Fable 5.
This is a 17-place jump from Kimi-k2.6 (#18 -> #1).
In Frontend, Kimi-K3 ranked #1 in 6 of 7 domains: Brand & Marketing, Reference-Based Design, Data & Analytics, Consumer Product, Simulations, and Content Creation Tools, landing #2 only in Gaming behind Fable 5.
The full model weights will be released by July 27.
Congrats to the @Kimi_Moonshot team on this major milestone!
I gave the same web app design task to both claude-opus-4.8 and chatgpt-sol-5.6, and both of them use same placeholder names: maya, northstar, atlas, nora... weird... and they came up with pretty much the same design...
Adithya Krishnan retweeted
As engineering, product, design, DS, etc. melt into a new kind of role, I was reflecting on what roles might look like in the future. For example, when I look at the Claude Code team I see what I think is five archetypes:
1. Prototyper: comes up with brand new ideas; churns out many ideas, most of which don't ship
2. Builder: quickly turns a prototype/idea into production-grade product/infra
3. Sweeper: cleans up the UI, simplifies the code and system, unships, optimizes performance
4. Grower: takes a product that has been built and iterates on it to improve Product-Market Fit
5. Maintainer: owns a mature system to make it secure, reliable, fast, and efficient as it scales
Many people span across 2 roles, and sometimes 3 roles. I also notice that these roles are not really tied to job function -- eg. across Anthropic, some designers match category 1, some 2, some 3; same for engineers, PM, DS.
A healthy team needs a mix of these, depending on the product:
- A product that is new and pre-PMF needs people that are strong at 1+2+3
- A product that is growing and has found PMF needs 2+3+4 and some 5
- A product that has strong PMF needs 3+4+5 and some 2
Maybe product roles of the future will look more like this, and less like the domain-specific roles of today?
Adithya Krishnan retweeted
People ask me how I stay so optimistic. The honest answer: I read the data, not the headlines.
What would this look like for molecules?
Adithya Krishnan retweeted
notes
The general loop is do good work and then tell people about it. Example (4M views teapaw.org/trq212/status/20274637… - 1-2 months of work, 2 days of writing)
Sowing:
- doing good work, even in a niche, is interesting to people not in the niche. (example: best pokemon player talking about his craft)
- use [TOOL] for everything, solve and find all problems
- talk to customers who were using [TOOL] inefficiently. realized that there were basic things they werent doing right, and it felt too basic to write but it reached a wide audience because he solved a pain point that customers had but employees maybe wouldnt experience
Reaping:
1. Tell a story. (not simply "how we built x", "how to do x" - more informative/subtle than that)
2. Keep it as simple as possible
3. Don't sell something you don't believe in - if you dont use it, dont write about it.
4. Share secrets - some "real alpha" that you're even a little scared to reveal to competitors
misc:
- Q: How do you write? A: very unstructured... "you have nothing until you have everything", range from
- Q: how much time do you spend writing for work? A: very spiky - one week a lot, one week just heads down engineering, aim for 50-50 sowing-reaping
- Q: how do you make diagrams? A: Claude Design - make 5 different SVGs, make a concept and choose one. diagrams, code snippets. use principles of claude design, give design system and references.
- Q: how much time you reading articles? too much marketing slop. "i read our slack alot".
- Q: getting feedback before posting - use ABCD feedback framework.
- Q: series - Lessons from Claude Code
- make sales collateral
- Q: did u make something more / less popular than expected? -> the skills post
TLDR: Build implicit knowledge, and then make it explicit.
Adithya Krishnan retweeted
There's never been an investment like the investment in railroads. (This graph has a log scale!)
claw mode ...
i can't believe more people aren't talking about this part of the claude code leak
there's a hidden feature in the source code called KAIROS, and it basically shows you anthropic's endgame
KAIROS is an always-on, *proactive* Claude that does things without you asking it to.
it runs in the background 24/7 while you work (or sleep)
anthropic hasn't turned it on to the public yet, but the code is fully built
here's how it works:
every few seconds, KAIROS gets a heartbeat.
basically a prompt that says "anything worth doing right now?"
it looks at what's happening and makes a call: do something, or stay quiet
if it acts, it can fix errors in your code, respond to messages, update files, run tasks...
basically anything claude code can already do, just without you telling it to
but here's what makes KAIROS different from regular claude code:
it has (at least) 3 exclusive tools that regular claude code doesn't get:
1. push notifications, so it can reach you on your phone or desktop even when you're not in the terminal
2. file delivery, so it can send you things it created without you asking for them
3. pull request subscriptions, so it can watch your github and react to code changes on its own
regular claude code can only talk to you when you talk to it. KAIROS can tap you on the shoulder
and it keeps daily logs of everything.
> what it noticed
> what it decided
> what it did
append-only, meaning it can't erase its own history (you can read everything)
at night it runs something the code literally calls "autoDream."
where it consolidates what it learned during the day and reorganizes its memory while you sleep
and it persists across sessions. close your laptop friday, open it monday, it's been working the whole time
think about what this means in practice:
> you're asleep and your website goes down. KAIROS detects it, restarts the server, and sends you a notification. by the time you see it, it's already back up
> you get a customer complaint email at 2am. KAIROS reads it, sends the reply, and logs what it did. you wake up and it's already resolved
> your stripe subscription page has a typo that's been live for 3 days. KAIROS spots it, fixes it, and logs the change
endless use-cases, it's essentially a co-founder who never sleeps
the codebase has this fully built and gated behind internal feature flags called PROACTIVE and KAIROS
i think this is probably the clearest signal yet for where all ai tools are going.
we are heading into the "post-prompting" era
where the ai just works for you in the background
like an all-knowing teammate who notices and handles everything, before you even think to ask
Great read for anyone working in AI for science. While many in this space are building fine tuned models and harnesses, the verifiability of the intermediate steps are the biggest bottleneck.
How would you build an RL environment for physics, chemistry?
We’re launching with two new posts.
Can AI do theoretical physics?
Harvard physicist Matthew Schwartz led Claude Opus 4.5 through a graduate-level calculation. AI can’t yet do original work autonomously, but it can vastly accelerate it.
Read more: anthropic.com/research/vibe-…
Adithya Krishnan retweeted
Introducing TigerFS - a filesystem backed by PostgreSQL, and a filesystem interface to PostgreSQL.
Idea is simple: Agents don't need fancy APIs or SDKs, they love the file system. ls, cat, find, grep. Pipelined UNIX tools. So let’s make files transactional and concurrent by backing them with a real database.
There are two ways to use it:
File-first: Write markdown, organize into directories. Writes are atomic, everything is auto-versioned. Any tool that works with files -- Claude Code, Cursor, grep, emacs -- just works. Multi-agent task coordination is just mv'ing files between todo/doing/done directories.
Data-first: Mount any Postgres database and explore it with Unix tools. For large databases, chain filters into paths that push down to SQL: .by/customer_id/123/.order/created_at/.last/10/.export/json. Bulk import/export, no SQL needed, and ships with Claude Code skills.
Every file is a real PostgreSQL row. Multiple agents and humans read and write concurrently with full ACID guarantees. The filesystem /is/ the API.
Mounts via FUSE on Linux and NFS on macOS, no extra dependencies. Point it at an existing Postgres database, or spin up a free one on Tiger Cloud or Ghost.
I built this mostly for agent workflows, but curious what else people would use it for. It's early but the core is solid. Feedback welcome.
tigerfs.io