Co-founder @Calibre_Labs | Applied AI research & consulting | Agents, AI Evals | prev EVP @amplitude_HQ | VC @khoslaventures @sequoia | @stanford @iitbombay
Joined May 2009
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Having too many people in a company or team creates huge drag for AI software - both creation and adoption.
We are going to see a lot of this in 2027 as public markets start looking at revenue per employee numbers as well as ROIC.
But before you can take any step like this you need to build your company's AI systems and upskill/acquire agent development teams - most software companies still haven't taken these critical steps, it's going to be forced on them by the market rather than in their own time.
Microsoft offers buyouts to 60,000 employees. This latest exit offer is available to far more employees than the previous retirement one, and comes just months after Microsoft laid off 4,800 employees. Full details here 👇theverge.com/tech/1018666/mi…
I was a big fan of the BYOK model for software but this week has made it very clear that storing LLM API Keys with vendors (Opencode, Railway) or even in your own proxy (LiteLLM) just paints a huge target on their back.
So what are we going to do about this? i can see o-auth style protection working for enterprise customers with token permissions scoped to specific vendors and usage types becoming a thing but that’s not going to scale well to the long tail.
Feels like this strengthens the traditional seat based software model with tokens baked into pricing where the vendor owns security. Building your own internal apps or giving vendors keys is just yet another attack surface now.
AI long form writing with a good skill/examples has now gotten good enough with a few models that i actually like having it to do the final-but-one draft.
in a few months (weeks?) pangram scores will be irrelevant. we will still see slop but wether AI was used will no longer be the right flag.
love seeing a new open source success 🫡🇺🇸
We just served 1 trillion tokens of Beam in 24 hours. To celebrate, we've reset everyone's limits.
Go build something big 👉 platform.reflection.ai
congrats Hamel! you’ve been such a big inspiration for me and @justinjbauer as we work with clients on AI ❤️
$META is going to have a good q4
product release videos got fully automated at the same time grokbot started monitoring twitter for us.
nobody can say this timeline doesn’t have a deep sense of irony.
🙌 no eval rubric == no product strategy in AI
These days, you can pretty much solve any task by defining an eval and hillclimbing over it, instead of directly defining the deterministic/agentic workflow to solve it.
The data provider companies' entire job is to define evals for all economic activity to make the frontier models capable of doing anything.
Your job then becomes pointing frontier intelligence in the right direction - defining what to solve, and how to measure what good looks like.
Agent application interfaces will evolve to capture this. The most complex processes will still need some sort of explicit workflow builder interface, but most tasks can be compressed into goals and eval instructions.
Sandhya retweeted
Introducing OpenSource Clay killer
- 85% cheaper than clay
- Most accurate on people-search bench
- 25x faster
- Run GTM directly in claude code or codex
Try it at treg.to/people-search
Repo link below 👇
Big day for 🇺🇸 OSS 🫡
Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active.
- Frontier reasoning efficiency
- Advances the Western open frontier on coding & agentic tasks
- Trained end-to-end from scratch
Full weights release this month.
Learn more about Beam: reflection.ai/beam
