Three days after a closed hit launched, an open-source version from someone who says "I built this a year ago" hit 35k stars: Laya and the name it grabbed
What it is An open-source small model that runs locally: give it a passage, ask it a few multiple-choice, scoring, or yes/no questions, and it returns a probability for each answer in tens of milliseconds.
Why it took off It grabbed the "open-source Jev" name first and told a story of "I built it first, and a big lab stole the spotlight," which put it at the top of HN. Later someone used it to play Snake on a Mac, and that video got 3M views. Various derivative projects all link back to it.
CHow you can follow Stop cloning Jev. Apply this playbook to the next closed hit: ship an open-source version within 72 hours, put the target's name right in the title, and release the SDK and demo within the first week. The cheapest way to follow is a port that runs on local devices, plus a playable video.
- Data (updated daily) · Repo created 2026-09-18 · 35,177 stars · 7 days +417
GitHub · stars Data 2026-09-27 → 2026-11-08 (43 days, 32 observations). Points before 10-28 come from deeply / archive backfill, not every day has one
In 2026 Sep 15, a company that had just raised $40M released a closed model called Jev. Three days later, the CEO of a small Indian company repackaged a paper of his that nobody had read a year earlier, released it as an "open-source Jev," and attached a long post with a combative title. 49 days later it had 35,056 stars, and the dozen or so "open-source Jev" projects that appeared the same week had fewer combined. But its model isn't better than the others'. Below: what it won on, where the stars came from, and whether you can still follow it.
A combative long post
Sep 18 UTC morning 4:46, GitHub user NandhaKishorM (Nandakishor Mukkunnoth, CEO of India's Convai Innovations) created the laya repo. 19 minutes later the model went live on Hugging Face, and an hour later he published a long post on dev.to titled:
I Built Non-Autoregressive Decision Models a Year Ago. Then a Frontier Lab Called It a "Breakthrough".
The second paragraph read: "Seeing the buzz online, I feel both vindicated and really frustrated." He listed his two arXiv papers from March and September of 2025, the weights and dataset released at the time, and an r/LocalLLaMA post. Then he named TypeSafe AI: its founder is a "co-inventor of ChatGPT at OpenAI," and at launch it had "no technical paper, no open weights, no training data." He closed with: "So this is what open source looks like, huh? 🙂"
The next day he posted "I built non-autoregressive decision models with RL a year ago" on Hacker News, which got 1363 points and 319 comments. This was Laya's biggest traffic source.
(One detail: his paper from 2025 predicted deal-closing probability in sales conversations, for one industry only. Jev does general decision-making. Calling them "identical" is a stretch, and some people on HN argued Jev is actually closer to the GLiNER approach. The dev.to post first called the model "RL Agent"; the name Laya came later.)
What it actually is
A 421M-parameter small model built on ModernBERT-large. You give it a passage and ask typed questions: choice (for example, which department should this ticket go to), score (for example, urgency on a 0–3 scale), or noul (yes or no, for example, is this email phishing). The model computes everything in one pass and returns a probability for each answer directly. It generates no text, so it never outputs broken JSON. On a T4 GPU, one call takes about 33 ms. Apache 2.0 license, open weights, runs locally.
Jev does the same thing as a hosted API: $0.042 per million tokens, 64K context, and much higher zero-shot accuracy.
A dozen "open-source Jev" projects in the same week
As soon as Jev launched, developers wanted to know whether there was an open-source version they could run locally. That gap stayed empty for only three days before a crowd filled it:
| Project | Launched | Stars (11-06) | How it differs from Laya |
|---|---|---|---|
| jaredpalmer/kev | 09-17 | 9,839 | Based on Qwen, can be trained yourself |
| TheoLeeCJ/SemIf-OpenJev | 09-16 | 4,929 | A "semantic if" running on 3090, narrower scope |
| deepopen-com/deepopen | 09-21 | 3,096 | Chinese packaging, description nearly copied from Laya |
| TianyuCodings/NanoJev | 09-17 | 2,579 | A small clone for teaching |
| nokia-applied-research/AnyJev | 09-21 | 1,586 | No training; wraps any LLM to behave like Jev |
| firelex/jeff | 09-28 | 1,578 | 0.8B, 576 points on HN, 10 days late |
| Mapika/decider | 09-16 | 1,077 | Qwen fine-tune |
| wfzyx/von | 09-18 | 921 | Same day as Laya, 40 minutes earlier, also claims sub-15 ms latency; no story |
| GLiNER2.5-Decide (Fastino) | 09-24 | — | 340M, company product, didn't claim the Jev label |
(GitHub, 11-06)
von's case says the most. Same day, and even earlier than Laya, same positioning, similar technical approach. Result: 921 stars, versus Laya's 35,056. The gap isn't about the model.
Stars don't match capability
The 0.766 accuracy in the marketing: flowtivity measured it in Sep 21 after fine-tuning on the benchmark's own training set. The base model without fine-tuning scored only 0.362, below the 0.461 you'd get by guessing the most common class. Its context is only 512 to 1K; Jev's is 64K. @tdinh_me (200k followers) had it and Jev play Tetris on a MacBook: "It's really fast (about 84 ms), but also much dumber." It lost all three games.
So most of those 35k stars are picking sides and curiosity, not production use. Still, the pip package laya got about 70k downloads in nearly 7 days (pypistats, including mirrors and CI), so plenty of people are trying it.
What it won on: the name, the story, and videos others made for it
First, it grabbed the name "the open-source Jev." The repo's topics literally include jev. On the afternoon of Sep 18, Hugging Face's Victor Mustar posted "is this the open weights Jev? (didn't try yet)" with a HF link, and it got 16k views. Derivative project READMEs and alternatives directories like opensourcealternatives.to kept repeating that positioning.
Second, it had a story that could start an argument. "Independent researcher vs. a lab valued in the billions," plus TypeSafe's launch language, "Breakthrough" and "can't hallucinate," which HN already found overhyped. Many people starred it to take a stance. Even critics helped spread it: @kunchenguid posted "poor guy claim to have built Jev a year ago but no one cared" with 230k views, while the body argued its accuracy was "about as good as a coin flip." People argued over whether marketing matters more than product. Whichever side won, the thing being discussed was Laya. Reddit even had a thread titled "Laya came first… so why is everyone still only talking about Jev?"
Third, it runs locally and fast, which makes good demos. This is where the long tail came from. In Sep 20, Chinese influencer @mizorewww (5,107 followers) posted laya-mlx: "50× faster than Jev, runs on your device," "uses at most 1G of memory," with a video of the model playing Snake at 60 decisions per second on his M3 Max. The post got 3.02M views, 12k likes, and 16k bookmarks, and laya-mlx itself got 7,070 stars. The next day, the official account of a local-model app, @atomic_chat_hq, reused the video with "Local Laya moggs Jev at Tetris," claiming it was 11× faster than cloud Jev on a 16GB MacBook Air. 900k views; it was really an ad for their app. Then came laya-coreml, the TypeScript port receptron/laya (1,040 stars), Ollaya (618 points on HN), a Home Assistant plugin, and Rust, Go, and C# ports, each linking back to the main repo.
Fourth, the author covered every integration point in the first week. v0.2.0 came out the next day, and seven weeks later there were 60 releases and 2,879 commits. pip, npm, Docker, MCP, LangChain, LlamaIndex, CrewAI, ONNX, Colab, and HF Space were all covered. Most other clones have one repo and a README. Anyone who wanted to "try the open-source Jev" in any stack could install Laya in one line, and anyone building derivatives would pick it as a base.
Star curve: most of them came in the first month
| Timeline | star | Notes |
|---|---|---|
| 09-18 → 09-21 | 0 → 6,000+ | Three days (wilsonwu.me) |
| 10-17 | 33,148 | Over 90% of stars came in the first month |
| Late October | +100 to +170 per day | |
| 11-06 | 35,056 | +424 in the last 7 days, +672 in the 7 days before that |
(Starting at 10-17 are GitHub's daily numbers; September's day-by-day curve could not be reconstructed)
The author has 2,054 followers, and his other public repos mostly have 0 to 4 stars. This one repo is his entire GitHub reputation.
If it were you
Verdict: C. You can learn a complete playbook for getting stars, but Laya itself is already late. The "open-source Jev" slot was filled by a dozen projects in September. Ports to other languages, MLX, CoreML, and TS versions have all been built, and stars are fading. And the key piece, the "I built it first" story supported by the author's own year-old paper, can't be taken by anyone else.
Who it suits: research-minded developers who already have a half-finished model, or who have a real "I built this long ago" history, and who can ship the model, SDK, and demo together within 72 hours.
Who it doesn't suit: anyone who wants to build yet another "open-source Jev" now (the dozenth, with no audience); anyone who only wants to port Laya to another language (most of the ecosystem already exists); anyone unwilling to get into public arguments.
How to do it, ranked by worth:
- Apply the playbook to the next closed hit: watch the HN front page and big-company launches. When something closed and API-only appears and developers ask for a local version, ship an "open-source X" within three days, put the target's name in the title and topics, and cover pip / npm / MCP / Colab / a live demo in the first week.
- Fix the part Laya got wrong: its zero-shot accuracy is weak and its context is short. If you can build an open small model whose zero-shot results on public benchmarks really approach Jev's, and put the comparison table on the first screen of the README, that gap is still open. It is real research, though, and hard.
- Build a derivative to ride the next wave: laya-mlx's author got 7,000 stars from a Snake video, more than any clone outside the main repo. The cheapest route next time an open hit appears: port it to run on a Mac or phone, and add a game demo anyone can understand at a glance.
How to get users: a long dev.to post and an HN Show post telling a story with a conflict; publish the weights on Hugging Face and hope HF staff reshare; prepare a video under 30 seconds that sells "runs locally, fast, fun to use", e.g. a game or real-time control of something, and let influencers in the Chinese and MLX circles spread it.
When: before: no fixed date, the window follows the event. The first 72 hours after a closed-source hit launches are the most valuable; after a week the slot is basically taken.
What would mean we got it wrong: ① Laya keeps adding 100+ stars a day through December, or a big company publicly uses it in production, which would mean it's more than a single spike and its ecosystem (plugins, reviews, fine-tuning tutorials) still has room to grow; ② TypeSafe releases Jev's weights, and the "open-source Jev" positioning disappears, so Laya's stars stop; ③ the next closed-source hit launches and its "open-source versions" generally fail to reach a few thousand stars, which would mean this playbook has been overused.
Data and sources
GitHub: NandhaKishorM/laya repo page, commit and release history, daily stars (10-17 to 11-06), star counts of similar repos (11-06). dev.to: author 09-18 long post. Hacker News: item 49765348 (1363 points), Jev launch item 49717558 (1989 points), Ollaya, jeff. X: @victormustar 09-18, @mizorewww 09-20, @kunchenguid 09-20, @tdinh_me 09-21, @atomic_chat_hq 09-21. Reviews: flowtivity.ai (09-21), wilsonwu.me. TechCrunch 2026-10-09 (Jev funding). pypistats (laya), Hugging Face.
Research notes: competitor table, acquisition evidence, full research report
- Competitors
Name Where Launched Scale How it differs from this one Jev(TypeSafe AI) Other 2026-09-15 HN 1989 points; October funding $870M, valuation $7.5B (TechCrunch) Jev is a closed hosted API billed per token, 64K context, more accurate zero-shot; Laya is open source, runs locally, faster, but weaker GLiNER2.5-Decide(Fastino) Open source 2026-09-24 launch tweet 150k views also an encoder decision model (340M), but without Jev's narrative jaredpalmer/kev Open source 2026-09-17 9,839 stars based on Qwen3.5/3.8, can be trained yourself; no "original author" story TheoLeeCJ/SemIf-OpenJev Open source 2026-09-16 4,929 stars a narrower semantic if on consumer GPUs wfzyx/von Open source 2026-09-18 921 stars launched the same day with the same positioning, but no story and no integration surface firelex/jeff Open source 2026-09-28 1,578 stars, HN 576 points 0.8B, trained at home, 10 days late BERT fine-tuning / LLM JSON mode Big-company features Already around — the traditional route means labeling your own data and fine-tuning; Laya pitches typed decisions that work out of the box - Where users come from
- Verified platform event: riding the Jev launch: Jev launched 09-15; Laya created its repo 09-18, published the HF model and a dev.to long post, and wrote the Jev comparison directly into the name and description
- Verified HN: "I built non-autoregressive decision models with RL a year ago", item 49765348,09-19,1363 points / 319 comments
- Verified dev.to story post: "I Built Non-Autoregressive Decision Models a Year Ago. Then a Frontier Lab Called It a Breakthrough", 09-18
- Verified X main accounts and derivative authors: @victormustar (HF) 09-18 posted "is this the open weights Jev?"; @kunchenguid 230k views; @mizorewww's laya-mlx snake video 3.02M views; @atomic_chat_hq's Tetris post 900k views (x-radar)
- Verified derivative ecosystem: laya-mlx 7k stars, laya-coreml, receptron/laya, Ollaya (HN 618 points), plus Rust / Go / C# ports, all linking back to the main repo
- Verified Reddit / Medium / YouTube / SEO: r/LocalLLM "Laya came first…", r/hermesagent; multiple Medium posts, flowtivity, eesel, YouTube "Laya vs Jev" videos
NandhaKishorM/laya: an "open-source Jev" that appeared 3 days after the closed Jev launch, plus an "I built this a year ago" story
Research date 2026-11-06. Channel: GitHub.
One line
Laya is an open-source "System 1 decision model" by Nandakishor Mukkunnoth (GitHub NandhaKishorM), CEO of the small Indian company Convai Innovations. It's built on ModernBERT-large, 421M parameters. You give it a piece of text and a few typed questions (choice picks one option / score rates / noul yes or no), and in one forward pass it returns structured answers with probabilities, no text generation, about 33 ms on T4. 2026-09-15, TypeSafe AI (founder Diogo Almeida claims to be a co-inventor of ChatGPT) launched the closed Jev, which instantly became the talk of the internet. Three days later, on 09-18, the author repackaged a paper on the same idea he had published 2025 years earlier, which almost nobody read, as an "open-source, locally runnable Jev", with the story "I built this a year ago, then a frontier lab called it a breakthrough". HN 1363 points, 6k stars in three days, 33k stars in a month, now 35k.
Data
- Our GitHub repo (repo_profile / repo_day): created 2026-09-18, 11-06 has 35,056 stars, 3,135 forks (forks/stars about 9%), and 47 open issues. 10-17 already has 33,148 stars, meaning over 90% of the stars came in the first month. Late October it gained 100~170 per day, November 50~75 per day, and in the last 7 days +424 (the first 7 days +672), so it's fading. The single-day peak in our records is +222 on 10-10, but the September burst falls outside our recording window. trending_days_any=0: we never saw it on trending, because trending only includes repos about 60 days old.
- Early velocity: "passed 6,000 GitHub stars within three days" (https://wilsonwu.me/blog/2026/jev-vs-laya/). Our token lacks access to the GitHub stargazers API, so the daily curve couldn't be reconstructed (unverified).
- Author account: 87 public repos, nearly all of the rest with 0–4 stars (GitHub API); owner_total_stars 35,350 is almost entirely this one repo; followers 2,054.
- Very high maintenance intensity: 2,879 commits, 60 releases (v0.2.0 09-19 → v0.4.27 11-06); also published the pip package
laya(pypistats: 30k downloads in the last 272 days, 7k in the last 70 days, including mirrors and CI) and the npm packagelaya-ts, plus HF models (convaiinnovations/laya with 28,508 downloads, 5,913 likes; laya-multilingual with 16,747 downloads), an HF Space demo, Colab, a docs site, integrations with LangChain / LlamaIndex / CrewAI / MCP / ONNX, and recently a "vegaml" reader based on Qwen3.5-0.8B plus image and voice input.
Competitors (doing the same thing)
| Name | Where | Launched | Scale | How it differs from Laya |
|---|---|---|---|---|
| Jev(TypeSafe AI) | closed API | 2026-09-15 | HN 1989 points / 520 comments (item 49717558); at the time $40M was raised at stealth, then $870M more in 10-09, valuation $7.5B (TechCrunch 2026-10-09) | hosted API, $0.042 per million tokens, 64K context, more accurate zero-shot; weights not released, no paper published |
| GLiNER2.5-Decide(Fastino) | Open source | 2026-09-24 | launch tweet 150k views (x-radar) | 340M encoder, does the same job; some on HN argued Jev itself is closer to the GLiNER lineage |
| jaredpalmer/kev | Open source | 2026-09-17 | 9,839 stars | based on Qwen3.5/3.8, can be trained yourself |
| TheoLeeCJ/SemIf-OpenJev | Open source | 2026-09-16 | 4,929 stars | a "semantic if" running on 3090 |
| TianyuCodings/NanoJev | Open source | 2026-09-17 | 2,579 stars | a teaching-oriented small re-implementation |
| nokia-applied-research/AnyJev | Open source | 2026-09-21 | 1,586 stars | no training; wraps any LLM into a Jev-style interface |
| firelex/jeff | Open source | 2026-09-28 | 1,578 stars; HN 576 points | 0.8B, trained at home |
| Mapika/decider | Open source | 2026-09-16 | 1,077 stars | Qwen3.5 fine-tune |
| wfzyx/von | Open source | 2026-09-18 (same day as Laya, 40 minutes earlier) | 921 stars | also claims "open-source Jev, under 15 ms", but has no story and no integration surface |
| deepopen-com/deepopen | open source (Chinese) | 2026-09-21 | 3,096 stars | description nearly copied from Laya, repackaged in Chinese |
| traditional approaches: BERT fine-tuning / zero-shot classification, LLM + JSON mode | Open-source / big-company offerings | Already around | — | the main criticism on HN was simply "this is just BERT with more data" |
(Star counts from the GitHub API / our github repo data, 2026-11-06.)
Takeaway: over the same week a dozen "open-source Jevs" appeared, and Laya alone got more stars than all the other open-source clones combined.
Why it took off
1. Timing: riding a closed hit that had just gone stealth and was being discussed everywhere (platform event)
- Jev launched 09-15 with 1989 points on HN, and MindStudio, LangChain, Olostep, Medium and YouTube quickly published explainers. The question developers most wanted answered was "is there an open-source, locally runnable Jev?"
- Laya's repo was created at 09-18 04:46 UTC, the dev.to long post went up 05:52, and the HF model went live 05:05: three things almost at once. On 09-18 14:11, Hugging Face's Victor Mustar (@victormustar) posted "is this the open weights Jev?" with an HF link attached (x-radar, 16k views).
- The positioning it claimed was "the open-source Jev". Later the receptron/laya README read "the open-source, Jev-compatible System 1 decision model", and opensourcealternatives.to listed it as an open-source alternative to Jev and Cohere Classify, so others are repeating the same positioning.
2. Story: "I built this a year ago, and a frontier lab called it a breakthrough"
- The dev.to post is titled "I Built Non-Autoregressive Decision Models a Year Ago. Then a Frontier Lab Called It a "Breakthrough"", citing its own arXiv paper: 2503.23303 (2025-03) and 2510.01237 (2025-09), along with an r/LocalLLaMA post that had only been posted once back then.
- The HN post "I built non-autoregressive decision models with RL a year ago" (item 49765348,09-19, 1363 points / 319 comments, linking to laya.convaiinnovations.com) was Laya's largest single traffic source. The comments mainly argued that "marketing matters more than the product" (johnfn), "the author is a bit salty" (dcow), and "Jev is really just GLiNER repackaged" (kamranjon). The story itself was what drew the argument.
- On X, @kunchenguid posted "poor guy claim to have built Jev a year ago but no one cared…" (09-20.2M views, 2204 likes), carrying the underdog narrative further. On Reddit there was "Laya came first… so why is everyone still only talking about Jev?" (r/LocalLLM), and r/hermesagent had "I'd rather use Laya than Jev", both saying essentially "Laya is the original Jev".
- Independent researchers vs. frontier labs valued at billions. Plus HN found TypeSafe's launch language overhyped ("Breakthrough", "two years in stealth", "can't hallucinate"), so many people starred it to take a side.
3. It runs locally and makes a good demo, which spawned a derivative ecosystem (that's the long tail)
- Chinese-language: @mizorewww posted 09-20 introducing laya-mlx, "50x faster than Jev, runs on your device," with a video of it playing Snake at 60 decisions per second on an M3 Max, 3.02M views, 12k likes (x-radar). The repo mizorewww/laya-mlx has 7,070 stars, and there's also laya-coreml (1,632 stars).
- @atomic_chat_hq posted "Local Laya moggs Jev at Tetris" on 09-21, claiming the local model runs 11x faster than cloud Jev on a 16GB Mac, with 900k views.
- Derivative projects include receptron/laya (TS, 1,040 stars), Ollaya (618 points on HN), FluidUse, ipenywis/laya-ultrafast, laya-playground, home-assistant-laya, plus ports to Rust, Go, C#, and Jolt, and Chinese projects like WechatVibe. Every derivative links back to the main repo.
- Media and SEO: Medium (multiple posts), flowtivity, eesel.ai, aiweekly, zimaspace, wilsonwu, and several "Laya vs Jev" comparison videos on YouTube.
4. The author covered a lot of integration surface and kept shipping
- v0.2.0 came out the next day. Seven weeks, 60 releases and 2,879 commits. pip, npm, Docker, MCP, LangChain, LlamaIndex, CrewAI, ONNX, Colab, and HF Spaces are all covered. Anyone who wants to "try an open-source Jev" can install it in one line on any stack, which other clones don't offer. The pinned README links a dev.to article and Buy Me a Coffee.
Star count doesn't match real capability (this matters)
- flowtivity's test (09-21): the advertised 0.766 accuracy came from a checkpoint fine-tuned on the benchmark's own training set. The zero-shot base scored only 0.362, below the 0.461 majority-class baseline. wavect.io also noted that "the three-day gap is the release date, not the development timeline."
- @tdinh_me compared them on Tetris: Laya lost all three games to Jev, "really fast but much more stupid." @paraschopra benchmarked it against Qwen3-4B. @ivanfioravanti pointed out its context is only 1K, versus Jev's 64K. On HN, some commenters said Laya was "significantly worse."
- So the stars reflect taking sides and curiosity, not production use. Still, pip gets 70k downloads a week, so plenty of people are trying it.
How many people have seen it
It got wide discussion, not a niche run. Evidence: an HN post with 1363 points, several derivative HN threads, multiple Medium, dev.to, and YouTube pieces, TechCrunch coverage of Jev's funding round as context, derivative posts with million-scale views on X, and several Reddit threads. The open-source project has no revenue, just Buy Me a Coffee. Copycats showed up early: over a dozen projects positioned as "open-source Jev" alone, plus a Chinese copy, deepopen-com/deepopen.
Differentiation
- Compared with Jev: open weights, Apache 2.0, runs locally, low latency, no per-token billing, supports 100+ languages. The tradeoff is much lower accuracy and context.
- Compared with other open-source clones: the model itself has no clear edge. von and Verdict are also ModernBERT-style approaches. What set it apart were three things: ① the "original author" story backed by an arXiv paper; ② the broadest and fastest-updated integration surface; ③ grabbing the name "the open-source Jev" in the first 09-18 hours. Those three things drove the traction, not model quality.
What to borrow
- Within 72 hours of a popular closed-source product launch, while the open-source slot is still open, ship an "open-source X" and name the target directly in the title and description.
- Lead with a conflict-driven personal story ("I built it first", "independent researcher vs. the giants"), post it on HN and dev.to, and let people argue.
- Cover pip, npm, MCP, LangChain, Colab, and HF Spaces in the first week to lower the bar for derivative projects. Derivative authors then spread it within their own language communities. One Snake video in the Chinese-language scene reached 3M views.
- To retain people long-term, you need steady releases and feature expansion (here, vegaml and multimodal). Otherwise interest decays once the hype passes.
Unknowns
- We don't have the daily star curve from 9-18 to 10-17, so we couldn't pin down which day or which channel contributed most.
- The claim that it was "built a year ago" was not verified against Jev's actual technical lineage. Some HN commenters argued Jev is closer to GLiNER.
- The 9% fork rate is high. Whether that comes from the derivative ecosystem and tutorial forks or from something else is unverified.
- How much of the pip download count is real usage is unverified.
Sources
- Repo github.com/NandhaKishorM/laya; GitHub API; our github repo_profile / repo_day (2026-11-06)
- dev.to dev.to/nandakishor_m_6cc0adfde9f/i-built-non-autoregressive-… (2026-09-18)
- HN news.ycombinator.com/item?id=49765348 (1363 points); Jev launch news.ycombinator.com/item?id=49717558 (1989 points); Ollaya news.ycombinator.com/item?id=49848269; Jeff news.ycombinator.com/item?id=49883844
- flowtivity review flowtivity.ai/blog/laya-open-source-jev-alternative/
- TechCrunch techcrunch.com/2026/10/09/the-maker-of-non-text-ai-model-jev…
- X (x-radar): x.com/mizorewww/status/2101473552956555427, x.com/atomic_chat_hq/status/2102160983409955244, x.com/kunchenguid/status/2101534610710761923, x.com/victormustar/status/2100950888357437668, x.com/tdinh_me/status/2101919182824804560
- wilsonwu.me/blog/2026/jev-vs-laya/ (6k stars in three days); pypistats laya; Hugging Face API