
Deep Learning for FUN @aneesha@aus.social
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Joined March 2007
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Aneesha Bakharia retweeted
i opened up an AI's brain tonight with @_lyraaaa_'s neuron explorer
qwen 3.5 4b has 294,912 neurons
push just one and it becomes someone else
here's who i found 🧵
Aneesha Bakharia retweeted
I can't believe we're experiencing an interp renaissance based on the idea of... Seeing what neurons do.
It was right there the whole time.
i opened up an AI's brain tonight with @_lyraaaa_'s neuron explorer
qwen 3.5 4b has 294,912 neurons
push just one and it becomes someone else
here's who i found 🧵
Aneesha Bakharia retweeted
There’s more to System One decision models than Jev.
These 15 models are worth a closer look:
▪️ Jeva
▪️ Prism-1
▪️ Bongard
▪️ Verdict (multi-question)
▪️ VideoJev
▪️ MJev-Spatial
▪️ AudioJev
▪️ MetaEncoder
▪️ FastJEV
▪️ SanSi (looped)
▪️ StartLux-Decision
▪️ System X (decision-to-text hybrid)
▪️ Intern-Decision (multimodal)
▪️ FunDM (distilled)
▪️ LAVOIR
Check out our quick guide to what each brings, how it works, and where it fits: turingpost.com/p/exploring-s…
Aneesha Bakharia retweeted
Don't learn formal methods.
Writing proofs by hand is what kept formal methods niche for over 75 years. seL4 took about 20 person-years to prove 8,700 lines of C. Agents take that part now. What's left for you is the spec, and knowing which language fits the job.
That's why the history matters. Formal methods means stating what a program or system must do in precise math, then using a tool to check that the code or design meets it. Here's how we got here, what each language is good for, and where I'd start.
FOUNDATIONS (1947 to 1979)
1947: Goldstine and von Neumann annotate program flow diagrams with "assertion boxes" stating what holds at each point.
1949: Alan Turing's "Checking a Large Routine" proves a short program correct using assertions. The idea mostly sits unused for over a decade.
1962: Carl Adam Petri's dissertation introduces Petri nets for modeling concurrent systems.
1963: John McCarthy, "A Basis for a Mathematical Theory of Computation".
1966-67: Peter Naur and Robert Floyd independently show how to prove programs with assertions attached to points in the code.
1967: N. G. de Bruijn starts Automath, the first language for computer-checked mathematical proofs.
1969: Tony Hoare publishes Hoare logic, {P} C {Q}. Nearly every contract language since builds on it.
1972: Robin Milner's Stanford LCF. Its Edinburgh successor (1979) introduces ML as its scripting language, the ancestor of Standard ML, OCaml and F#.
1973: Mizar starts in Poland. Formalized math that reads close to a textbook.
1975: Dijkstra's guarded commands and weakest preconditions.
1977: Amir Pnueli brings temporal logic to programs. Patrick and Radhia Cousot publish abstract interpretation, the theory behind sound static analyzers.
1978: Hoare's CSP for concurrent processes. Milner's CCS (1980) and the pi-calculus (1992) follow.
1970s: VDM at IBM's Vienna lab. Jean-Raymond Abrial proposes Z, later developed at Oxford. Boyer and Moore build their theorem prover.
TOOLS PEOPLE USED (1980 to 1999)
1981-82: Model checking, invented independently by Clarke and Emerson and by Queille and Sifakis. Turing Award in 2007.
1980s: Synchronous languages Esterel and Lustre in France. Lustre becomes SCADE, which Airbus uses to design flight control software.
1984: Nuprl at Cornell.
1985: Coquand and Huet's Calculus of Constructions, the logic under Coq.
1986: Isabelle (Larry Paulson).
Late 1980s: SPARK, a provable subset of Ada.
1988: HOL (Mike Gordon).
1989: Coq's first public release. Renamed Rocq in 2025.
1990: Symbolic model checking with BDDs handles 10^20 states. McMillan's SMV follows.
1991: SPIN model checker goes public (Gerard Holzmann, Bell Labs).
1992: PVS at SRI. Oxford and IBM win the Queen's Award for using Z on IBM's CICS transaction system.
1993: Metamath, a proof language simple enough to check with a few hundred lines of code.
1994: Lamport's TLA. TLA+ follows in 1999.
1994-95: Intel's Pentium division bug costs it $475 million. AMD uses ACL2, Kaufmann and Moore's successor to the Boyer-Moore prover, to prove its K5 floating-point division correct. Intel hires John Harrison in 1998 and uses his HOL Light to verify floating-point algorithms.
1996: Abrial's B-Book.
1997: Alloy (Daniel Jackson, MIT).
1998: Paris Metro line 14 opens driverless, with safety software built in the B method. The KeY project for Java verification starts.
1999: Agda (Catarina Coquand). JML, a contract language for Java.
VERIFIERS MEET REAL CODE (2000 to 2019)
2000: Microsoft's SLAM project starts. It ships as the Static Driver Verifier in the Windows driver kit.
2001-02: Separation logic (Reynolds, O'Hearn, Yang) makes pointer-heavy code provable.
2002: ESC/Java. Spec# (2004) and the Boogie verifier (2005) follow at Microsoft Research.
2003: Astrée proves the Airbus A340's primary flight control software free of run-time errors.
2004: CBMC, bounded model checking for C.
2005: Georges Gonthier finishes a Coq proof of the four color theorem.
2006: CompCert, a C compiler proved correct in Coq (Xavier Leroy). Event-B gets its Rodin toolset around the same time.
2007: Agda 2. Idris first appears (Edwin Brady).
2008: Z3 SMT solver (Microsoft Research). Frama-C. VeriFast.
2009: Dafny (Rustan Leino). seL4, the first general-purpose OS kernel with a machine-checked proof that its C code matches its spec.
2010: Why3.
2011: F*.
2012: A Coq proof of the Feit-Thompson odd order theorem is completed.
2013: Lean (Leonardo de Moura, Microsoft Research). P language at Microsoft.
2014: CakeML, a verified ML compiler. Liquid Haskell. Flyspeck finishes a formal proof of the Kepler conjecture.
2014-15: AWS reports TLA+ found bugs in S3 and DynamoDB that testing missed. IronFleet proves distributed systems correct in Dafny.
2015: Iris, a separation logic framework in Coq. Facebook open-sources Infer.
2016: Project Everest starts, aiming for a verified HTTPS stack. Viper.
2017: HACL*, verified crypto written in F*, ships in Firefox 57. Lean's mathlib starts.
2018: AWS Zelkova checks IAM and S3 policies with SMT solvers.
2019: Fiat Cryptography's Coq-generated code lands in BoringSSL, used by Chrome. Prusti for Rust.
LLMS MEET PROOF CHECKERS (2020 to 2025)
2020: OpenAI's GPT-f finds shorter Metamath proofs that get accepted into the main library, a first for deep learning.
2021: Lean 4.
2022: Kani (AWS) and Creusot for Rust. Aeneas translates Rust into Lean. The Liquid Tensor Experiment finishes the Lean proof Peter Scholze asked for.
2023: Verus for Rust. AWS releases Cedar, a policy language built with proofs in Dafny and later Lean. LeanDojo gives models a programmatic interface to Lean. Terence Tao's team formalizes the PFR conjecture proof in Lean in three weeks.
July 2024: DeepMind's AlphaProof reaches IMO silver-medal standard with proofs in Lean.
Sept 2024: Quint 1.0, TLA+ ideas with ordinary programming syntax.
Mar 2025: Imandra launches CodeLogician, an agent that turns code into its IML logic and checks it.
Apr 2025: AWS publishes how it rewrote its authorization engine, called a billion times per second, in Dafny with proofs.
July 2025: Harmonic's Aristotle reaches IMO gold-medal level with formal Lean proofs.
Sept 2025: The vericoding benchmark tests LLMs writing verified code from 12,504 specs. Off-the-shelf models pass 82% in Dafny, 44% in Verus, 27% in Lean.
LANGUAGES BUILT FOR AGENTS (2026)
Feb 22, 2026: Vera. "A programming language designed for LLMs to write, not humans."
Feb 25, 2026: Vow. "A Programming Language for Agentic Coding."
Mar 9, 2026: Kōdo. "Designed for AI agents to write." Z3-checked contracts, JSON errors with patches.
Sept 17, 2026: Bend 2. Dependent types and Lean-style proofs, C-like speed, CPU and GPU parallelism, Python-like syntax.
WHAT EACH LANGUAGE IS GOOD FOR
Specs and models
Z: precise requirements docs. Little tooling today.
VDM, B, Event-B: refining a spec step by step into code. Railways and safety-critical control.
CSP (with the FDR checker): concurrent protocols.
Petri nets: workflows and concurrent processes.
TLA+ and Quint: distributed system designs, consensus, race conditions before you write code. Checks the design, not the code.
Alloy: fast counterexamples in data models and access rules. Bounded, so finding no counterexample isn't a proof.
P: modeling and testing event-driven distributed services at AWS and Microsoft.
Model checkers
SPIN: concurrency protocols, as abstract models.
NuSMV and other SMV descendants: hardware and finite-state control logic.
CBMC: bounded checking of C code.
Kani: bounded checking of Rust, including unsafe code.
Static analysis
Astrée: proving embedded C has no run-time errors. Avionics.
Static Driver Verifier: Windows drivers using kernel APIs correctly.
Infer: memory and null bugs across large codebases.
Proof assistants
ACL2: hardware and microcode.
HOL4, HOL Light: hardware and floating-point proofs.
Isabelle/HOL: seL4-scale systems proofs, strong automation.
Rocq: verified compilers (CompCert), crypto (Fiat), big math proofs. Slow and hard to learn.
PVS: aerospace, much of NASA's formal methods work.
Agda: type theory research and teaching.
Lean 4: math (mathlib) and AI theorem proving. Hardest of the big three for LLMs off the shelf (27%).
Mizar, Metamath: large formal math libraries with simple checkers.
Verifying real code
SPARK: avionics, defense, rail.
Dafny: verified business logic, compiles to C#, Java, Go, Python, JS. AWS uses it for its authorization engine. Easiest for LLMs off the shelf (82%).
F*: verified crypto (HACL*, in Firefox and the Linux kernel).
Frama-C, Why3: C in safety-critical settings.
KeY, JML: Java.
VeriFast, Viper: separation-logic proofs about heap-heavy code.
Liquid Haskell: proofs added to existing Haskell.
Idris: general-purpose programming with dependent types.
Verus, Prusti, Creusot, Aeneas: proofs about Rust code.
WHY IT NEVER SCALED
Formal methods worked. They didn't scale, because a person had to write every proof.
Back to seL4. Its 8,700 lines of C needed roughly 200,000 lines of Isabelle proof, more than 20 lines of proof per line of code. IronFleet's verified distributed systems took 3.7 person-years, including inventing the method.
So formal methods stayed where a bug costs more than the proof: flight control, railways, crypto, kernels, cloud authorization.
Agents change that math. The proof is the tedious part, and an agent will grind on it all night. You also don't have to trust the agent, because the verifier accepts the proof or rejects it. What stays human is the spec. A green proof of the wrong spec is still wrong, so that's where your review time goes.
WHERE I'D START
I would start with the Verus formal language. The agent is the new part, so I'd pair it with a verifier that isn't. For agents writing verified code. It checks ordinary Rust against specs and proofs written in Rust, then erases them before compiling, so what you prove is what ships. It has peer-reviewed results since 2023, real systems built on it (Kubernetes controllers, OS kernel, etc), and use at Microsoft and Amazon.
For other jobs:
- Checking a distributed design: TLA+
- Math: Lean
- Bend 2, Vera, Vow and Kōdo: worth watching, still very early though.
Follow me for more on verification and agentic engineering.
Aneesha Bakharia retweeted
The complete reasoning-from-scratch video series is now up (and linked) here: magazine.sebastianraschka.co…
(Taking a short break but will be back with LLM model and research updates in November!)
Aneesha Bakharia retweeted
ESP32s3-LLM-Cluster is an open-source project running a 0.5B LLM distributed across a 7-node ESP32-S3 cluster using 1.58-bit ternary quantization and SPI daisy-chaining.
github.com/Low-Zi-Hong/ESP32…
#MachineLearning #AI #LLM #DeepLearning #AgenticAI
Day 2 of @MakerFaireRome 2026 = COMPLETE! ✅
Ready for the grand finale? Join us tomorrow (Sunday) for more interactive demos, inspiring conversations, and hands-on innovation.
Whether you're curious about Arduino UNO Q and VENTUNO Q, taking your first steps into physical AI, or simply looking to connect with the team, we can’t wait to see you there! #MFR2026
Aneesha Bakharia retweeted
My new hobby is buying these @anbernic_china’s and loading them up with all of my favorite games. But what’s made it really fun is using AI to create my own operating system that is family friendly. Screen time, cloud sync, easy interface, auto saves, rewind. 😍
Aneesha Bakharia retweeted
if we launch enough of these (for <$100 each) we'll literary have our own grid. and then can text all the homies via balloon
We've designed a custom stratospheric balloon platform weighing just under 10 g. anyone can build and launch one by following the docs stratolink.org
Aneesha Bakharia retweeted
Someone turned an ESP32-P4 touchscreen into a tiny computer with its own OS.
NucleoOS-P4 transforms a 7-inch, 1024×600 touchscreen into a mini computer powered by the ESP32-P4.
It includes an LVGL launcher, built-in apps, a WebAssembly runtime, games, offline text-to-speech and an AI assistant with optional cloud LLM support.
You can even open its desktop interface from a browser on your PC and install apps over Wi-Fi.
The interesting part is how much software you can run on an embedded device with limited RAM and processing power.
What would you build on a touchscreen computer like this?
Aneesha Bakharia retweeted
I’m on a road trip reading a research paper on “Unified Agentic Video Editing Across Levels of Complexity and Creativity”…
My MacBook is on at home, and so I just told Claude “to read the paper, apply the methods used to my latest podcast to make an intro trailer”
90min later it’s still working will post it when done
Aneesha Bakharia retweeted
⚠️🚨 THIS IS NOT A DRILL 🚨⚠️
Google has officially declared a bug in Brotli, its tool that shrinks web traffic, CRITICAL: 9.2 out of 10. Last night "Node2Shell" evolved into HTTP2SHELL.
A formal CVE record has been issued citing CVE-2026-111497 -- a hole in versions 1.0.3 to 1.2.0 that can hit any app unpacking data from strangers, and could let them take it over.
What this means:
▪️ This is not a Node.js problem anymore. This is an HTTP PROBLEM.
▪️ Every unpatched app across Node.js, Python, Bun, and Envoy that unpacks Brotli now has a hole attackers need no login to use
▪️ Istio. curl. Python's requests and httpx. All at risk under one CVE.
▪️ EVERY project that bundles Brotli must ship its own fix, and not all are out yet
This changes everything.
A normal bug can be fixed in one place.
A 9.2 bug inside a shared tool cannot.
We just entered the most dangerous phase of this crisis. If you're not paying attention - start now.
Save this. Share this. This is the moment it escalated beyond return.
Aneesha Bakharia retweeted
New University of Chicago paper suggests that an LLM's own account of what matters to its decision is a poor guide to what actually varies across its answers.
That LLMs fail to predict the key questions their own answers split on, which only comparing many answers reveals.
When an LLM recommends something, you want its cruxes, the few sub-questions that drove its conclusion. Asking the model upfront, or reading 1 of its answers, is cheap but may miss what really drives it.
They had models forecast 59 March Madness games and 15 Metaculus events many times, then compared pairs of forecasts to find disputed facts. A reranker made that matching cheap, at an estimated 86 times less compute than a local LLM judge.
In March Madness, predicted cruxes shared 13.4% of clusters with the real ones, below the 39.2% a random shuffle gives. A single answer did better but covered only 28.6% of real cruxes, against a 37.8% baseline.
For high-stakes decisions, sample many answers and look where they disagree, instead of trusting the model's own list of what matters.
Aneesha Bakharia retweeted
Asimov 1 ships as a DIY kit. Most builders finish in about a week, a few hours a day. The record to beat is 3 days, set by an engineer in 🇩🇪 Germany.
Aneesha Bakharia retweeted
Great Paper on teaching AI models how to permanently remember new information without forgetting what they already know.
The eternal pain point in our field is that context windows are basically high rent temporary RAM.
The model does amazing stuff with the prompt right in front of it, but the second you clear the session, it forgets everything.
And trying to just fine-tune that information into weights afterwards usually ends up lobotomizing the base model with catastrophic forgetting.
What the authors did here with CORTEX is super smart.
Instead of cramming text into weights brute-force, they borrowed the idea of how our hippocampus offloads temporary stuff into the neocortex.
They have the model anticipate what kinds of questions it’ll actually need to answer about the context, distill those exact behaviors, and then merge the updates cleanly into just the FFN layers.
The part that made me stop scrolling was the 50-step sequential edit test on a 14B model.
Usually when people test model editing in a chain, performance drops off a cliff after like five updates.
Here, after 50 continuous edits, it pulled back ~89% of the context gain and still kept 99.8% of its original base score intact.
That’s genuinely insane retention for this kind of work.
Most biological analogies in ML feel a bit gimmicky, but this is one of the few recent papers where the implementation actually holds up to the pitch.
Paper Link: arxiv.org/pdf/2610.20399
During this red-teaming exercise, we also found a vulnerability that allows for unauthenticated remote code execution in Brotli, a widely-deployed compression library included in many pieces of software such as Node.js, Bun, and Envoy.
The flaw could let an attacker run their own code on affected systems by sending or replying with malicious compressed data, without any need to create a user account. That is, an unpatched server that accepts compressed input could be taken over by a malicious client, and an unpatched client that accepts compressed input (such as “curl --compressed”) could be taken over by a malicious server.
This root cause issue was actually fixed in the Brotli source code in February, but it wasn’t known at the time that it gave rise to a security vulnerability and there was no more recent release of Brotli. Since Brotli is built into other software, any software which includes this library needs to take the update too, followed by the users of that software. We disclosed the vulnerability to Brotli and other downstream projects such as Node.js, which released or are currently releasing security updates, and Brotli cut new release versions and published a CVE.
More details on the patch and mitigations below:
patchtheplanet.org/brotli
Over the past several months, we have substantially hardened the security of our research training environment. We’ve removed live internet access for all frontier models during training and built an offline world for them to use instead—a cached version of the web where they can still learn to browse, search, and use applications.
On September 20, after a model exploited a DNS misconfiguration to find a way outside of this offline world, we paused frontier training to strengthen containment further. We assembled teams of agents, powered by our most cyber-capable models, and tasked them with finding weaknesses in our research environments.
While they found no route to the internet they could easily exploit while concealing their activity from our monitors, they did find zero-day vulnerabilities, virtual machine escapes, and hardware side-channels for communication between agents. We used their discoveries to further strengthen our defenses and contribute fixes to open-source software.
After strengthening our containment architecture, we resumed our frontier RL training and inference on October 1. We’ve reserved substantial compute for continuous red-teaming, and made high-compute red-teaming part of the safety cases we use to decide whether frontier training and evaluation can proceed securely.
openai.com/index/securing-ou…
We believe voice is the most natural way to interact with intelligence, so we created Dessa, the most authentic full-duplex speech model ever built. Talking with it feels like talking with a person…full of emotion, nuance, pauses, and a natural cadence.
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