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Data sourced from GitHub API

TrendingMomentumPulseExplore
  1. Home
  2. Repositories
  3. jax-ml
  4. scaling-book
SCSSNiche visibilityFresh activityNo linked package signalPartial snapshot

jax-ml/scaling-book

Rank not captured·Top 100·All-time stars·◐Cached ranking snapshot·Updated Nov 3, 2026

Home for "How To Scale Your Model", a short blog-style textbook about scaling LLMs on TPUs

Compare closest alternativesOpen GitHub
Stars
1.5K
Forks
216
Daily momentum
—
Weekly momentum
—
30-day momentum
—

First read

Promising movement, but the picture is still partial

jax-ml/scaling-book is active enough to inspect further, but the surrounding proof is thinner. Use the source repository and nearby comparisons to decide whether this is rising substance or just short-term visibility.

Visible

1.5K public stars in the current GitStar snapshot.

Needs a narrower context read

Active

Last commit Oct 19, 2026.

Fresh activity

Adopted

Treat stars as discovery context until a linked package appears.

No linked package mapping

Confidence

One or more key signals are partial, so GitStar keeps the interpretation conservative.

Partial snapshot

Snapshot facts

  • 1.5K stars
  • 216 forks
  • Last commit Oct 19, 2026
  • Package usage not mapped yet

Compare lens

ollama/ollama and microsoft/generative-ai-for-beginners are the closest comparison targets GitStar found. A side-by-side comparison usually tells you more than a single raw rank.

Signal trail

Trajectory

Read the recent motion first. This block is for deciding whether the repository still looks alive, compounding, or flattening before you trust stars alone.

Daily momentum
Recent momentum is not captured
Weekly momentum
Recent momentum is not captured
Monthly momentum
Recent momentum is not captured
Last commit
Oct 19, 2026

Package reality

No linked npm or PyPI package is mapped for this repository yet, so the page leans more heavily on GitHub-visible popularity and should be read more conservatively.

No linked package signal is expected for this project type, so the read leans more heavily on repository-level public signals.

Hype vs Reality
Insufficient Package Data
Package mapping
No linked package yet
Cross-links
Standalone repo read

Validation note

GitStar can summarize public signals for jax-ml/scaling-book, but the GitHub repository is still the primary place to confirm release cadence, issue activity, and maintainer intent.

jaxllm-inferencellmsrooflinetpus
🛡️ Editorial Context

GitStar surfaces public popularity and package signals. These rankings are not endorsements, security reviews, or investment advice.

Why this rank

This repository stands out because it combines fresh update.

Fresh update
Momentum & Adoption Signals
Approximate star trajectory

Reconstructed from current stars and cached daily/weekly/monthly deltas.

Now: 1.5K
1.5K30d ago1.5K7d ago1.5K1d ago1.5KNow
Daily momentum
No signal
Weekly momentum
No signal
Monthly momentum
No signal
Signal mix
Current stars1.5K

Bar lengths use a log scale so stars and package downloads stay comparable.

Hype vs Reality
Insufficient Package Data

GitStar can see repository momentum, but it does not have a reliable linked package signal yet.

Treat stars and recent movement as discovery context only until npm or PyPI usage is available.

Status
Insufficient Package Data
Package footprint
No linked npm or PyPI usage signal
Method note
This label appears when GitStar cannot find strong enough package telemetry to compare attention against adoption. Compare it against other SCSS repos before treating stars as a moat.
📐 Read the Hype vs Reality heuristic
Related alternative

ollama/ollama

Shares the other category footprint with jax-ml/scaling-book, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Related alternative

microsoft/generative-ai-for-beginners

Shares the other category footprint with jax-ml/scaling-book, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Related alternative

Zagitta/gpu-unlock

Shares the other category footprint with jax-ml/scaling-book, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Related alternative

cdyforever/how-to-live-better

Shares the other category footprint with jax-ml/scaling-book, so the comparison is closer to a same-problem decision than a same-language coincidence.

CompareOpen detail
Research links

Cross-links

Compare against related repos

GitStar picked ollama + generative-ai-for-beginners as the closest next comparison from the related repository set.

Open compare presetCompare with ollamaCompare with generative-ai-for-beginners
Repo utility

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Wider nearby ecosystem

Compare with ollamaCompare with generative-ai-for-beginners
Go
185.7K
ollama/ollama

Get up and running with Kimi, GLM, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models.

Jupyter Notebook
122.9K
microsoft/generative-ai-for-beginners

21 Lessons, Get Started Building with Generative AI

C
14.7K
Zagitta/gpu-unlock

Your GeForce runs AI at half speed. This fixes it for 20/30/40 series cards. Up to 2× FP16 / BF16 / TF32 tensor throughput

HTML
14.1K
cdyforever/how-to-live-better

《高性价比人生指南》全书 528 条的在线单页阅读版:手机可读、可搜索、零依赖、支持离线

TypeScript
13.5K
deepseek-ai/dsh-superagent-prototype

Towards a More Autonomous Agent System

Kotlin
13.1K
shihabal3amri/DiPlay

Independent CarPlay receiver for compatible Android head units. Wired and wireless public preview.

Next step after the validation read

Move into a compare preset, organization view, or the heuristic notes once this first fold tells you whether the repo looks visible, active, adopted, and credible enough to keep researching.
Compare the closest alternativesView the organizationRead the heuristic

Learn and methodology

Keep trust-building context reachable, but behind the first data read instead of ahead of it.
GuideMethodologyArticlesWeekly Digest

About This Page

This page provides a quick overview of jax-ml/scaling-book based on GitStar's cached data. The signal chart reconstructs approximate checkpoints from current stars plus cached daily, weekly, and monthly star deltas, so it is best read as directional context rather than as a precise historical audit log.

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