anthropics/FlashProve · anthropics

Anthropic uses an unmaintained repo to prove "the model answering you is the model": where 904 stars came from and what you can take away

What it is Research code released by Anthropic: the provider locks in a secret set of model weights, and every reply then comes with a zero-knowledge proof. Anyone on an ordinary computer can verify in seconds that "this reply was really computed by that model." The repo states it is not maintained.

Why it took off An official Anthropic blog post plus an official tweet (685k views) drove the traffic. It sat on the HN front page for a day, and crypto accounts it name-checked amplified it. It hit a suspicion people already had: is the model behind the API being quietly swapped?

CHow you could ride it Don't touch the zero-knowledge proof itself. Build the part it doesn't cover: a tool that checks today whether a relay API has been swapped out, plus a public leaderboard, shipped while the discussion is still hot.

C grade: its stars came from Anthropic's brand and official channels, and the technical bar is cryptographer-level, so copying it won't get you there. Competing repos in the same week didn't move at all, which shows the attention didn't spill over. What you can learn is the launch packaging, and the "cheap substitution detection" gap next to it is worth trying, but that has little to do with its own hype. Public / widely discussed

Data (updated daily) · repo created 2026-10-30 · 953 stars: GitHub · stars
Data 2026-11-04 → 2026-11-08 (5 days, 5 observations). Points before 10-28 come from deeply / archive backfill, not every day has one

Nov 3, Anthropic released a research prototype: an AI provider can attach a proof to every reply, and anyone can verify on an ordinary CPU that "this reply really came from that model," without seeing the model weights. It got 904 stars in three days, nearly all from Anthropic's own official channels. Below: what it is, where the stars came from, who else is in this space, and what an ordinary developer can take away from it.

0.02 and 110

In Nov 4, the Hacker News front page's #2 post was "Progress on provable inference for large language models," with 169 points and 84 comments, and it stayed on the front page for nearly a day. The sharpest comment came from James Petrie, author of VerInf. He had earlier proved the same 4 Maverick model, a 400B-parameter model, at about 0.02 tokens/second, which means 1000 tokens took over 14 hours. He accused FlashProve of "not even crediting 400B Maverick, barely attributing it."

FlashProve's number on the same model is about 110 tokens/second (prefill throughput on a single H100, batching 24 requests). That's thousands of times faster. This comparison, along with "verifying one proof takes 17 seconds on a CPU" and "one proof is 19 MB," became the figures that kept recurring in media headlines.

The repo was created in Oct 30 and went public with the blog in Nov 3. 11-04 had 257 stars, 11-05 jumped to 765 (+508 in a single day, the day the HN post hit the front page), and 11-06 is at 904 stars and 83 forks, with only 139 added that day (GitHub, 11-06). A classic launch curve: spike on the day, then fade.

What it actually is

The first line of the README: FlashProve lets an AI provider prove which model answered a request, without leaking the weights. It works in three steps:

Commit: the provider publishes a commitment ID, bound to a secret set of weights.
Prove: for every reply, the provider's GPU generates a proof.
Verify: anyone can check it in seconds on a CPU with cp_verify, no weights needed.

The protocol's soundness is formally proven in Lean 4. Among the collaborators is Alessandro Chiesa (EPFL, StarkWare co-founder, co-inventor of STARKs). The blog and docs state plainly that the code, the Lean proofs, and the protocol spec were written almost entirely by Claude under human direction, with no independent security audit.

The repo homepage states plainly: "Research output: not maintained, contributions not accepted." Running the prover for real takes a dedicated H100 and 280+ GB of memory, but verifying an existing proof only needs 2 GB of RAM and a two-minute build. The README has a section, "Built to be run by an agent," with a prompt you can hand to your own coding agent to build, verify, and report.

Put simply: this is companion code for a paper, not a tool built for people to use. Of the 904 stars, probably few have actually run it (inference; the repo has only 1 open issues).

Where the stars came from

Official Anthropic channels, which account for most of the traffic. The blog released five things at once: the article, a technical report, a protocol spec written by Claude, code, and Lean proofs, with the repo linked from the post. @AnthropicAI's Nov 3 tweet got 5,223 likes and 685k views (X, 11-06), and X even published a dedicated trending-topic page for it.

One day on the Hacker News front page. The post above. Interestingly, the same link had been submitted twice earlier in Nov 3 and only got 9 and 6 points. Making the front page was partly luck. Once it did, the plagiarism accusations, "is this paving the way for regulatory capture?", and "does Claude count as a contributor?" debates pushed the comments to 84.

Crypto accounts that were name-checked reposted it. The blog mentions Starknet, Mantle, and the STARK paper, and a collaborator is from StarkWare. So @StarkWareLtd, @Mantle_Official, @crypto_banter (titled "ANTHROPIC IS USING CRYPTO-STYLE ZK PROOFS," 28k views), Decrypt, and KuCoin news all shared it, framing it as good news that "a major AI company endorses zero-knowledge proofs." It was free cross-community distribution.

Personal accounts of collaborators and investors: collaborator Giacomo Fenzi got 240 likes and 12k views; investor Jamin Ball posted "Provable inference is coming."

What it didn't have: no ads, no influencers, no Product Hunt launch. The repo was only 7 days old and hadn't made GitHub's trending list.

Why this one

The technical lead is real: a 400B-parameter mixture-of-experts model, thousands of times faster than the previous work at the same scale, formal proofs, and it doesn't even reveal which experts were used. But the stars didn't come from any of that.

A telling number: the week FlashProve launched, star counts in the same space barely moved. Lagrange's deep-prove went from 11-01 to 11-06 3,351 to 3,354, EZKL stayed at 1,236, and PrimeIntellect's TOPLOC went from 63 to 64 (GitHub, 11-06). The attention didn't spill over to the zero-knowledge ML category at all. It all went to the Anthropic name.

What actually got people to star it is that it hit a suspicion everyone already had: how do you know the model behind the API is really the one you paid for? Examples that kept coming up in HN comments: third-party relays swapping models, "Opus got dumber again today," and Chinese Claude resellers watering down output. Until now that could only be judged by feel. Now someone has offered a cryptographic answer, even if it isn't usable today.

Who else is doing the same thing

Project	star(11-06)	How it differs
Lagrange DeepProve	3,354	A general-purpose framework from a crypto company, mostly GPT-2-class models, leaning on-chain
inference-labs subnet-2	2,463	An incentivized verifiable-inference network on Bittensor
EZKL	1,236	Compiles small ONNX models into circuits; can't handle large models
Kimi / K2 Vendor Verifier	601 / 197	Built by Moonshot AI; catches third-party APIs that quietly serve tampered models by comparing benchmark results. It's a statistical judgment with no cryptographic guarantee
PrimeIntellect TOPLOC	64	Uses locality-sensitive hashing to check whether a provider has swapped models or lowered precision. Much cheaper, but weaker security
VerInf	Under 50	Same Maverick, 0.02 tokens/second

There are also companies going the trusted-hardware route (NVIDIA confidential computing and the like). They're fast, but you have to trust the hardware vendor.

Read this table from both ends. The top layer, cryptographic proofs, belongs to big labs and cryptographers, and FlashProve has already pushed that ceiling to 400B. The bottom layer, "cheap, rough, usable right now": Moonshot's Vendor Verifier got 601 stars for that, and it targets exactly the general suspicion, yet few people are building there.

If it were you

Verdict: Building a zero-knowledge proof project like FlashProve's makes no sense. Its stars came from Anthropic's official tweet and blog, an entry point you don't have, and the technical bar is cryptographer-level. What's worth building is the part it doesn't do and doesn't plan to: a detection tool any developer can use today to answer "has my API been swapped?"

Who it's for: developers who use relay APIs daily and know each vendor's output habits well; people who can write evaluation scripts and understand sampling and statistics. Chinese developers have a natural edge here, since the domestic relay market is the largest and the complaints about watered-down output are the loudest.

Who it's not for: people who want to compete head-on on cryptographic proofs, or anyone who wants to make money from this (open source pays in stars and reputation, not revenue).

How to do it:

A command-line tool or a web page: enter a relay's address and key, run a carefully chosen set of probe questions (knowledge cutoff dates, specific formatting habits, refusal boundaries, token counts, logprobs fingerprints), and within minutes get a verdict on how closely it matches official Claude / GPT / Kimi, plus a confidence level.
Watch the gap that FlashProve and Kimi Vendor Verifier both leave open: relays for Claude and GPT, and a timeline of each vendor's "dumbing down." Run the same question set daily and plot the curve.
Publish a public leaderboard: real test results for a dozen or so common relay providers, updated weekly. The leaderboard itself is the shareable content.

How to get it seen: Copy FlashProve's packaging: repo, a long-form post, and raw test data together. Give it a sticky contrast number ("tested 20 relays, 7 weren't the model they claimed"). Add a README section with a prompt to "hand to your coding agent." Post on V2EX, Jike, and the Chinese AI circles on X, plus Show HN. Naming the providers you test will draw pushback, and the pushback is the traction. But the data has to be reproducible, or the first person to check will tear it apart.

Why now: FlashProve just made "provable inference" a topic. The discussion will likely run for a few weeks. After that, if Anthropic or OpenAI ships verification in their own APIs, the gap closes.

What would mean you read it wrong: A week after launch the stars stall at a few dozen, nobody reposts, and nobody argues. That would mean people say they're suspicious but don't actually want to test. Or a major lab announces verifiable responses in its official API and the mainstream relays follow. Then the detection tool has no reason to exist.

GitHub repo ↗

Research notes: competitor table, acquisition evidence, full research report

Competitors

Name	Where	Launched	Scale	How it differs from this one
Lagrange DeepProve	Open source	2024-11	3354 stars; claims to prove full GPT-2	A general zkML framework from a crypto company, mostly GPT-2-class models, leaning on on-chain narratives. FlashProve goes straight for a 400B MoE and positions itself on AI safety and regulation.
EZKL (zkonduit)	Open source	2022-07	1236 star	Compiles small ONNX models into zk circuits. Can't handle LLM scale.
inference-labs subnet-2	Open source	2024-04	2463 star	A verifiable-inference incentive network on Bittensor. Crypto-native mechanism design.
VerInf (James Petrie)	Open source	2026 (unverified)	Below 50 stars (not in our database)	Also proves Llama 4 Maverick, but only at about 0.02 tok/s. The author accused FlashProve on HN of copying their work with barely any credit.
zkLLM (CCS 2024)	Open source	2024-04	61 star	Academic prototype at the 13B scale, orders of magnitude slower.
PrimeIntellect TOPLOC	Open source	2025-01	64 star	Uses locality-sensitive hashing to check whether the model was swapped. Cheap, but no zero-knowledge and weaker cryptographic guarantees.
Kimi / K2 Vendor Verifier	Open source	2025-09	601 / 197 star	Catches API tampering by comparing against benchmark scores. It's statistical judgment, not proof.
Attestable and TEE routes (NVIDIA confidential computing, etc.)	Other	2025–	Not verified	Relies on trusted hardware. Fast, but you have to trust the hardware vendor. FlashProve is pure software cryptography.

Where users come from

Verified Anthropic official blog + official tweet: anthropic.com/research/provable-inference (11-02/03) links to the repo. @AnthropicAI's 11-03 tweet has 5223 likes, 539 reposts, and 685k views (x live).
Verified Hacker News front page: 11-04 169 points, 84 comments, best rank 2 on the front page, staying about 23 hours (hackernews database). Matches the +508 stars from 11-04→05. The comments section has plagiarism accusations and a debate over regulatory capture.
Verified Crypto ecosystem and KOL organic amplification: @StarkWareLtd 197 likes, @Mantle_Official 112 likes, @crypto_banter 205 likes / 28k views, plus Decrypt, KuCoin, and blockchain.news. The blog name-checks Starknet and Mantle, and collaborator Chiesa is a StarkWare co-founder.
Verified Collaborators' and investors' personal accounts: @GiacomoFenzi 240 likes / 12k views; @jaminball posted twice (61 and 25 likes).
Verified Tech press pickup: Help Net Security 11-04, Unite.AI, Interesting Engineering, technology.org, Yahoo Tech, Reddit r/machinelearningnews, YouTube Shorts.
Inferred GitHub trending: The repo is 7 days old, short of the roughly 60-day threshold. trending_days_any=0.

anthropics/FlashProve: why it blew up

Channel: GitHub | Research day 2026-11-06 | Data card: cards/gh_anthropics__FlashProve.json

One line

FlashProve is a research prototype Anthropic released for zero-knowledge proofs (zkSNARKs) of AI inference. The provider first makes a "commitment" to a set of model weights. After that, every response comes with a proof. Anyone can verify on an ordinary CPU in ten-odd seconds that "this response was actually computed from those weights," without ever seeing the weights. The stars come from a research launch. This repo is the companion code to Anthropic's research blog. There's no growth play, and the product isn't pulling people in.

Data

The repo was created on 2026-10-30,11-03, when the blog went public. Our snapshot (github database, repo_day): 11-04 has 257 stars, 11-05 has 765 (+508, the single-day peak), and 11-06 has 904 / 83 forks. 11-06 single-day growth has already dropped to +139, the typical decay curve of a launch event.
The repo is only 7 days old, short of the roughly 60-day threshold to appear on GitHub trending (card: trending_days_any=0). So every star came from outside traffic.
Declared version 0.16.0, "not maintained, not accepting contributions." Primary language is Lean 4, plus a C++ verifier and a CUDA prover.
Ranks mid-table among the repos Anthropic put out in the second half of 2026 on its own account. commerce-agents 3425, jacobian-lens 2119, oss-scanner 1708, fermats-last-theorem 1298, FlashProve 904 (GitHub repo_profile). You can see that this account's research companion repos hold a floor of a few hundred to a few thousand stars.

What it is

(Sources: repo README, Anthropic blog)

Three steps: commit (publish a commitment ID that binds a confidential set of weights), prove (H100 generates a proof for each response), verify (cp_verify checks it on a CPU in a few to ten-odd seconds, no weights needed).
Runs full-size Llama 4 Maverick (400B MoE, 4-bit) with a context of 1024 tokens on a single H100. Proving speed is about 110 tok/s, each proof about 19 MB, CPU verification takes 17 seconds (24 threads). Proving overhead is roughly 70–95× vLLM inference.
The protocol's soundness is formally proven in Lean 4. The blog and the known-limits doc both state that the code, the Lean proofs, and the docs were written almost entirely by Claude under human direction, with no independent security audit.
The README has a section, "Built to be run by an agent," that gives a prompt for a coding agent to build and verify the project. The whole document is written to a standard an agent can execute.
Collaborators include Alessandro Chiesa (EPFL / StarkWare co-founder, co-inventor of Zerocash and STARKs), Giacomo Fenzi (EPFL), and Miranda Christ (MIT), among others.
Background: In its Frontier Safety Roadmap, Anthropic publicly committed to building a provable-inference prototype by 2026-09-30. The blog says that goal was already met by a different project (signatures plus trusted-server recomputation), and FlashProve is another route.

Competitors

Name	Form	Launched	Scale	How FlashProve differs
Lagrange DeepProve	Open source + company	2024-11	3354 stars (github database 11-06). DeepProve-1 claims to prove full GPT-2, paper on IACR eprint 2026/1112.	A general zkML framework from a crypto company, aimed at on-chain and token narratives. Model scale is mostly GPT-2-class. FlashProve goes straight for a 400B MoE and positions itself on AI safety and regulation.
EZKL(zkonduit)	Open source	2022-07	1236 star	Compiles small ONNX models into zk circuits for developers. Can't handle LLM scale.
inference-labs subnet-2	Open source (Bittensor)	2024-04	2463 star	Attaches verifiable rewards to inference on a decentralized network. Crypto-native incentive design.
VerInf(James Petrie,MARS fellowship)	Open source	2026 (exact date unverified)	Not in our database (below 50 stars)	Also proves Llama 4 Maverick, but only at about 0.02 tok/s (1000 tokens take 14.3 hours). FlashProve is thousands of times faster, but the benchmark choices are the same. The author publicly accused FlashProve on HN of "copying down to the same 400B Maverick, with almost no credit."
zkLLM (code from the CCS 2024 paper)	Open source	2024-04	61 star	Academic prototype at the 13B scale, orders of magnitude slower.
ddkang/zkml	Open source	2023-02	379 star	Early academic zkML
PrimeIntellect TOPLOC	Open source	2025-01	64 star	No zk. Uses locality-sensitive hashing to check whether the provider swapped models or lowered precision. Much cheaper, but security is weak.
Kimi / K2 Vendor Verifier (Moonshot AI)	Open source	2025-09 / 2026-01	601 / 197 star	Catches third-party API tampering by comparing against benchmark scores. It's statistical judgment with no cryptographic guarantee.
Attestable, TEE route (NVIDIA confidential computing, etc.)	Company / hardware	2025–	Not verified	Proves things with trusted hardware. Fast, but you have to trust the hardware vendor. The Attestable founder said in an HN post that August had already published 87 t/s.

Verdict: someone is doing this, and quite a few of them, but nobody has the full combination of "a major lab personally doing it + frontier scale of 400B + formal proofs." What's more telling: in the week FlashProve launched, competitor stars barely moved. deep-prove went from 3351 to 3354 (11-01 to 11-06), ezkl stayed flat at 1236, and toploc went from 63 to 64 (GitHub repo_day). The attention didn't spill over into this space; it stayed with the Anthropic brand.

Growth tactics (where users come from)

Anthropic official blog + official tweet (main engine, verified). The blog went up on 2026-11-02 / 03, shipping five things at once: the blog post, a technical report, the protocol spec written by Claude, the code, and the Lean proofs. The repo is linked from the blog. @AnthropicAI's official tweet went out at 2026-11-03 20:10 UTC, with 5223 likes, 539 reposts, and 685k views (x live, 11-06). X also produced a trending topic page titled "Anthropic's FlashProve Verifies AI Outputs with Crypto Proofs."
Hacker News front page (verified). "Progress on provable inference for large language models" got 169 points and 84 comments on 11-04, best ranking 2 on the front page, staying in the top 90 for about 23 hours (hackernews database). The timing matches the +508 stars from 11-04 to 11-05. The same link was submitted twice more on 11-03 and got only 9 and 6 points, so whether a link takes off on HN is partly luck. The comments fought hard: some questioned the usefulness ("this is Anthropic's own problem"), some worried about regulatory capture, the VerInf author accused them of plagiarism, and some objected to listing Claude as a contributor. The fight itself added heat to the thread.
Organic amplification from the crypto world (verified). The blog name-checks Starknet, Mantle, and the STARK paper, and a collaborator is a StarkWare co-founder. So @StarkWareLtd (197 likes, 16k views), @Mantle_Official (112 likes), @crypto_banter (205 likes, 28k views, headline "ANTHROPIC IS USING CRYPTO-STYLE ZK PROOFS"), Decrypt, KuCoin news, blockchain.news, and Vietnamese-language KOLs all reposted it, pitching it as good news for ZK because "AI endorses ZK." The ecosystems that got name-checked did the distribution for free.
Co-author's personal account (confirmed). Giacomo Fenzi posted (240 likes, 12k views). Investor @jaminball also posted two lines: "Provable inference is coming" (61 likes) and "The world needs provable inference" (x library).
Tech media spread (confirmed). Help Net Security (11-04), Unite.AI (11-02, an AI-generated piece), Interesting Engineering, technology.org, Yahoo Tech, mlq.ai, cellcog, datastudios. Reddit r/machinelearningnews and YouTube Shorts also covered it (Jina search). Almost all of it restates the official blog post.
Not found: ads, creator promotion, TikTok / Xiaohongshu ops, Product Hunt. GitHub trending didn't pick it up either, since the repo isn't old enough yet.

Why it took off

The brand is the distribution. The repo sits under anthropics, and the official blog plus the 685k-view official tweet sent traffic straight to it. Comparable open-source projects don't get that entry point.
The topic hits a common anxiety. "How do you know the model behind the API is the one you're paying for?" HN comments keep raising cases of third-party relays swapping models, "Opus got dumber again today," and adulterated Claude resellers. Users already suspected it, and this is a cryptographic answer.
The performance numbers hit hard. Same 400B Maverick: the predecessor VerInf ran at 0.02 tok/s, FlashProve at 110 tok/s. Easy-to-remember comparisons like "verified in 17 seconds on CPU, on a single H100" went straight into headlines (shattered.io used "72x", kucoin used "19 MB / 17 seconds").
Two circles lit up at once. The AI safety / research crowd cared about regulation and pacing; the crypto crowd treated it as an endorsement of ZK going real. StarkWare and Mantle, which were named, had ready reasons to repost.
"Written by Claude" is itself a narrative. The code, Lean proofs, and spec were almost entirely produced by Claude. That became another showcase of model capability for Anthropic, in line with the earlier fermats-last-theorem and formal-math releases.
Controversy added heat. Plagiarism accusations and worries about regulatory capture on HN pushed comments to 84, and the post stayed on the front page for nearly a day.

Differentiation, and was it the reason it worked?

The technical differentiation is real: a 400B MoE scale, thousands of times faster than the same-scale predecessor, Lean formal proofs, weights hidden during the commit phase, proofs that don't reveal which experts the MoE picked, and docs written to be executable by agents. But those don't explain the stars. Competitors' stars didn't move in the same week, the repo says it's unmaintained, and few people can actually run the prover (it needs an H100 plus 280 GB+ of memory). Most stars look like "saw the news, bookmarked it, showed support." The differentiation gave media and HN numbers and a story worth writing about; what actually decided how many stars it got was Anthropic's official channels.

Visibility and copycats

Visibility: widely covered. The official tweet got 685k views, it hit #2 on HN, there was an X trending topic page, and more than ten tech and crypto outlets ran it. It's open-source research, not revenue-generating.
Copycats: none found. No forks renamed or cloned the project (a GitHub search for flashprove by name and description returns only the original). The reverse is happening: people in the same niche are riding along. Attestable promoted itself in the HN thread, and StarkWare and Mantle used it to back their own tech.

What others can borrow

The research release was bundled as "blog + technical report + code + formal proofs + visualizations," and the post named ecosystem partners (Starknet, Mantle, StarkWare). Named partners repost on their own, which amounts to cross-circle distribution for free.
It gave one memorable number with a direct comparison to the predecessor (0.02 to 110 tok/s), which made it easy for media to use in headlines.
The README includes a prompt, "paste this to your coding agent," which lowers the barrier to trying it and also shows agent-native engineering docs.
Negative lesson: mishandled attribution on the predecessor got it called out on HN on the spot.

Still unclear

Stars before the blog post (10-30 to 11-03) weren't tracked, so it's impossible to separate how many of the 11-04 257 stars came on launch day.
The VerInf plagiarism accusation isn't verified. FlashProve's acknowledgments thank James Petrie for VerInf, and one commenter (remcob) argued the two tech stacks are completely different.
No data on how many people have actually run the verifier or prover. The repo has 1 open issues and doesn't accept contributions.
Will Anthropic integrate it into its own API products (for example, attaching proofs to Claude responses)? No public plans seen.

This article was written by AI from public data and the data we collect daily. Numbers are sourced, and inferences are labeled.