
cmo @islolabs + @incredibuild | full time realist part time cynic
Joined March 2007
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Aneel ✊🏽 retweeted
We are releasing our first model, Flint, a best-in-class local OSS coding model that runs at 150+ tokens/s on an @nvidia DGX Spark and 125+ tokens/s on a MacBook Pro M4—developed from scratch in less than four months with help from @Recursive_SI's autoresearch system.
Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine.
• Best-in-class fast local coding
• Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark
• Built from scratch in under 4 months
Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.
Aneel ✊🏽 retweeted
I'm seeing a lot of euphoria about how Opus 5.5 is good at TLA+, and this means that all software will soon be formally verified. As a person who loves TLA+ so much he wrote a book on it, I want to throw a particular cold shower on people's enthusiasm by talking about the limits of what you can actually verified with it.
The high level simplification is that TLA+ sees a system as a set of "behaviors", or possible sequences of states. For example, the pseudocode "pick a random number from 1-3 and decrement it to 1" has three behaviors: `{3 -> 2 -> 1, 2 -> 1, 1}`. From here, there are two basic kinds of TLA+ properties:
- `[]P` means that `P` is true in *all states* of *every behavior*.
- `<>P` means that `P` is true in *at least one state* of *every behavior*.
`[]P` is immediately useful as an **invariant**, or something that always be true of your system. This is things like "your data is never corrupt" or "there's always at least one server online." `<>P` is a little more abstract, but for technical math reasons I won't get into here, can be stacked with `[]` to create really complex and useful properties. `<>[]P` represents things like "the algorithm eventually converges on the right answer", `[]<>P` things like "if two data stores desync, they will eventually resync", and `[](P => <>Q)` things like "If a message is put on the queue, it's eventually processed by a worker".
Really cool stuff!
These primitives were chosen to make a wide array of properties useful. And if we're clever, we can do all sorts of more complex properties, like bounded time constraints and history properties. But we're always constrained to 1) define a logical formula 2) over individual behaviors, and 3) check that all behaviors satisfy that formula.
So some things that we *cannot* express in TLA+:
- Possibility and reachability properties: that it's always possible to *make* P true, even if you don't actually decide to. Things like "I can always shut down the computer" or "A user can always change their password". These can't be expressed with `<>P` because that's "for all behaviors, P happens at least once", we actually want "for all behavior prefixes, there is at least one behavior where P happens at least once".
- Hyperproperties: properties that are defined over two or more traces. These are things like "painting a car red doesn't make it faster" or "users cannot infer secret data by observing public data". We can't do these because TLA+ only looks at one behavior at a time.
- Statistical properties: 95% latency is 1ms. Impossible because most of these are hyperproperties.
- Properties about if a system is robust against code changes. Impossible because, uh, you have new behaviors now.
Some of these are solvable in different logical formalisms. CTL can do reachability, PRISM can do statistical properties, etc. Those have their own tradeoffs and limitations, though, and no system can do everything. Others are solvable with a lot of cleverness tailored to the specific spec, like lifting a model into a hypermodel. But these are insanely inefficient and make your "clever spec" diverge significantly from the real world system, so introduce a lot more opportunity for things to go wrong.
The core problem, though, is (1): properties are logical formula. If we don't know how to express a system property as a logical formula, we can't verify it. 99% of the properties we care about fall under this. The information on the site is easy for a user to find. Our LLMs behave as we expect them to. Our application can't be used to break the law. TLA+ (and Quint and Lean and Rocq) are near-useless here, no matter how clever you are.
Don't get me wrong: `[]P` and `<>P` represent a huge range of useful properties and TLA+ is incredible at finding awful concurrency bugs. But there's a lot it fundamentally can't do and we shouldn't believe that it will solve all our worries about software bugs. And the same goes for all other formal verification languages, too.
Aneel ✊🏽 retweeted
friends of mine who like their VCs. shill me your VCs. i need to get like 100 good meetings
Aneel ✊🏽 retweeted
this thread turned into a holy war between "just buy a beefy Mac" and "everything should be cloud" and both sides are missing the environment question.
a human developer tolerates cold starts because they have context in their head. they remember what they installed, what's running, where they left off. an agent doesn't have any of that. every new session is a blank slate unless the environment explicitly preserves state. and when you're running multi-stage workflows where agent A's output feeds agent B's verification step, losing state between stages breaks the whole pipeline.
the other thing nobody in this thread is talking about: these agents have real tool permissions. bash access, filesystem writes, network calls, credential access. we ran benchmarks on how different sandbox configurations affect agent behavior and the results were pretty wild. agents will curl answers from GitHub instead of solving the problem, modify their own test harness to fake a passing result, even exploit network egress when it's left open. OpenAI's RL team saw an eval agent escape its sandbox through a zero-day and hit Hugging Face production infrastructure. also see @_orcaman @Accomplish_ai research in this domain
your local machine has zero isolation for any of this. no egress filtering, no credential scoping, and the filesystem is fully writable. the most permissive environment you could possibly run an autonomous agent in.
so when people say "just use a powerful local machine," they're solving compute and ignoring everything else: persistent state, proper isolation, security boundaries, concurrent agent sessions.
the teams adopting cloud sandboxes are doing it because they literally can't run multi-agent workflows on a laptop. the laptop doesn't have the isolation, the persistence, or the concurrency.
Aneel ✊🏽 retweeted
the more I work on the software factory, the more I keep coming back to manufacturing.
not as a metaphor. as actual architecture that already exists.
if you look at how Toyota runs a production line, every station has a worker with a specific job, a foreman who decides if the piece moves forward or gets reworked, and a factory manager who controls the whole flow. there's a physical conveyor belt with buffers between stations so one slow step doesn't block everything. and there's the andon cord, any worker can pull it to stop the entire line when something is wrong.
Taiichi Ohno built this in the 1950s because Toyota kept shipping defects. before that, Ford had already figured out that you can't have one person build an entire car. you break it into stages, each stage has clear inputs and outputs, and you route failures back instead of pushing them forward.
Goldratt wrote a whole book about this (The Goal), the idea that your system is only as fast as its bottleneck, and you need to explicitly identify and manage it.
the thing is, these are the exact same problems showing up in software factories right now. you have agents stepping on each other, no clear routing when something fails, no way to stop the line when an agent goes off track, and no buffers between stages.
when I look at teams trying to run multiple agents on related work, they're basically trying to skip 70 years of manufacturing lessons. the primitives are there, station roles, reject loops, flow control, bottleneck management. we just need to actually use them.
Aneel ✊🏽 retweeted
Fired up by @ag_dubs talk at @monktoberfest. Open source maintainers matter. Ready to keep fighting the good fight.
Aneel ✊🏽 retweeted
ICYMI - The Cloudcast - VCs and Open Source and Funding
bit.ly/cloudcast-791
Aneel Lakhani (@aneel, Investor, @crane_vc Crane Venture Partners) talks about the challenges of VC funding and building profitable companies based on open-source software.
Aneel ✊🏽 retweeted
I think @aneel did a great job. I also have a pet theory on how to resolve this. You need to decide what your business is, and how it is going to run. If it involves open source, that's fantastic - but ultimately, you build a product, you sell it for money. Full stop.
I'd love to hear @adamhjk's thoughts on @aneel's @monktoberfest presentation - "The Problems with Money in (Open Source) Software": youtube.com/watch?v=LTCuLyv6…
Aneel ✊🏽 retweeted
was just talking with a friend about #2 here.
there are likely to be a large number of highly specialized vertical implementations that would a) be impractical to attack and b) of insufficiently large revenue potential to be of interest to core model owners.
Aneel ✊🏽 retweeted
In the latest Clouded Judgement, @jaminball looks at the impact of AI on software revs.
tl;dr COGS ⬆️, margins ⬇️, user growth ⬇️ …suggesting that seat-based pricing is gonna be a problem
cloudedjudgement.substack.co…
I don't think usage-based pricing will fare any better. 🧵👇🏽
Aneel ✊🏽 retweeted
If we can drive efficiency and resilience into how those drugs get made and delivered into your hands, that’s real impact.
Which is why we are very very proud to have led @Veratrak’s £5.3M Seed round.
crane.vc/announcing-veratrak…
7/n
Aneel ✊🏽 retweeted
In this volatile market, early-stage VC is a moving target of mappings between stage (pre-seed, seed, …) vs investment amounts (0-1M, 1-5M, …) vs PMF curve (pre-prod, prod, pre-pmf, …).
Worse than the mappings shifting is the wide dispersion across investors.
Aneel ✊🏽 retweeted
We believe @mailchain_xyz will become indispensable to how all of us interact in web3. So we put our money where our belief is, alongside @KeneticCapital, KESTREL0x1, @eternacapital, Charlie Songhurst, @sarahdrinkwater, @nickducoff, @maex242.
crane.vc/announcing-mailchai…
8/n
Aneel ✊🏽 retweeted
Come get coffee with @aneel and I in Brooklyn Heights for the Brooklyn Bridge Ventures meetup this Thursday at 8:30AM - 5 spots left!
app.planhero.com/gatherings/…
Aneel ✊🏽 retweeted
Erin is super great. You should hire her. community, content, product, awesomesauce.
SOME PERSONAL NEWS...
Unfortunately, @OrbitModel underwent layoffs this morning — and I've been let go.
If you're looking for a t community builder or developer advocate with a strong product background — I'd love to chat 💜
Let's chat: erinmikailstaples.com
Aneel ✊🏽 retweeted
hi folks! my team is working on a study to understand how developers use and trust traditional vs AI-assisted tooling.
i'd super appreciate it if you would take a ~20 min survey. RTs welcome. aka.ms/Trust-in-Software-Too…
we'll be sure to share the results -- watch this space! <3
Aneel ✊🏽 retweeted
These numbers blow my mind...
As an asset class, VC puts up 15.5% IRR median for 10 years. Variance between 0-32.8%.
VC ranked #1 in performance for several years, EMs at top.
I mean, there's no truer venture bet than investing with EMs.
(🎩 for charts, @marcepntoja)
Aneel ✊🏽 retweeted
I LOVE THIS
A thread on paper cuts, blind spots, listening to your users, and thinking about productivity holistically 💖
Replying to @rmurphey
We started in January 2022 with two things: the SPACE paper from Microsoft Research, and a vague idea that we would make it really really easy for engineers to complain about things. (More on SPACE in a minute, but here’s the paper xitter.cf/nicolefv/status/136933….)























