I wrote a code-graph plugin for Claude Code and tested it against three popular ones on a real task. None of them helped.
Every Claude Code session starts the same way. The agent does not know my codebase, so it greps, reads files and guesses what a change will break.
I thought it would be great to give it a map. The agent asks one question, "what does this change affect?", and gets a short list of files and tests. The map is built from imports and from the contracts between packages:
- HTTP routes
- GraphQL operations
- tables
- queues
- env variables
So I built that tool and called it gsttt. A week later I had tables and charts in which it beat three popular tools. Then I tested all four on real work, and none of them made a measurable difference.
This post is the whole story, from the idea to the moral. It includes the test setup, so you can check my work or redo it on your own repository.
The short version
- The idea: a map of the code should help an agent find what a change affects.
- The tool: I built gsttt, and my own benchmark said it beat Graphify, GitNexus and codebase-memory-mcp.
- The first reality check: on nine real commits, the installed tool was never called: 0 of 36 runs.
- The last try: on one large real task, all four tools cost the same as no tool, inside the noise.
- The moral: the agent did not need the graph. The bill followed which model does the code reviews and how many review rounds run.
Contents
- [The idea and the build](#the-idea-and-the-build)
- [The first reality check](#the-first-reality-check)
- [One last try with a real task](#one-last-try-with-a-real-task)
- [The result](#the-result)
- [Did the agents use the tools](#did-the-agents-use-the-tools)
- [What moves the bill](#what-moves-the-bill)
- [The moral](#the-moral)
- [Limits](#limits)
- [If you want to reproduce it](#if-you-want-to-reproduce-it)
The idea and the build
The code-graph tools I found did not treat contracts between packages as first-class edges. A GraphQL field in the server and the client query that uses it are an example. I wanted that link in the map, so I wrote my own on October 28.
It is written in Rust, mostly with Claude Code.
To see if it worked, I built a small multi-language fixture repository. I added fourteen questions with truth lists, such as "what depends on this file". Mean recall:
| Tool | Mean recall |
|---|---|
| gsttt | 87% |
| GitNexus | 73% |
| codebase-memory-mcp | 72% |
| Graphify | 46% |
On Amplication, an open-source TypeScript monorepo, the TypeScript compiler also listed 97.3% of the callers gsttt found for 200 random methods.
I was pleased. Then I looked at what the benchmark measured.
I had written the fixture and the questions, knowing what gsttt does. A benchmark you write for your own tool measures whether it does what you built it to do.
The first reality check
So I ran the tool on real work: nine real merged changes, five from Amplication and four from a private monorepo. Each agent got a ticket written from the commit message, and I compared its diff with the real one. The model was Sonnet 5.5 at high effort, 99 runs in all.
- Installed only: the agent never called gsttt. 0 of 36 runs, and 0 in 107 real sessions of mine.
- With one sentence in the ticket ("Use gsttt to find what the change affects."), it called gsttt in all 27 runs.
- The result: 4.4 more turns, about 4% more money, and no better recall of the real files: 0.64 against 0.66.
I tried to fix that:
- a skill text that says when to call the tool
- answers in functions instead of files
- cheaper answers
- hints placed in the planning step and at stop time
Nothing helped beyond the noise.
An offline check on 420 commits compared gsttt with git co-change history. At the same list size, git history found 13 points more of the real files. For these agents, the graph carried no information they could not get from search.
But those were small commits. A large change across many packages might be where a map pays off.
One last try with a real task
I also wanted the other tools in the picture. They have between 48,000 and 142,000 GitHub stars, and gsttt has 0. So I picked one large real task and ran all four tools on it.
The task is a merged change in a private TypeScript monorepo for a SaaS product: an annual billing interval with a discount. It touches:
- price calculation, the payment integration and the checkout service
- a GraphQL contract on both sides, regenerated into three packages
- a plan picker, a pricing section and translations in five locales
- tests and an OpenSpec change proposal
The real diff is 36 files, +1,183 and -96 lines. I chose it from 377 recorded sessions in that repository. It was the cheapest of four that edited at least eight files across three packages without any tool.
The original session used Claude Opus 5.5 at xhigh effort. It took 57 turns and 15 sub-agents, cost about $8.10 and also made commits and a pull request.
The codebase has six packages and 2,809 tracked files, about 282k lines of TypeScript. It carries its own agent setup, which stayed in every run: a CLAUDE.md, two sub-agent definitions and nine skills.
The setup:
- Each run starts in a fresh git repository with one commit, the parent state. The agent has no network and cannot read outside its folder.
- The ticket is 335 words. Every run used Claude Opus 5.5 at xhigh effort, Claude Code 2.1.318, with a $15 cap.
- Plugin only: the tool is installed as its README says, and nothing else.
- Enforced (bare rounds only): the ticket also demands a tool call before the first edit and before the end. The gate never fired, because the agent called the tool first anyway.
Two environments. The first one was a mistake.
- Bare (rounds 1 and 2) loaded only the repository's own files. It is clean, but I never work that way, so its dollar figures do not describe my sessions.
- Harness (round 3) also loads my own setup: my agorch plugin and superpowers, the two plugins of the original session, plus my instructions and rules.
- In both, nothing else is loaded and nothing reaches outside the machine. Every run's start is audited against its arm.
The tools are these four, at the versions I ran:
| Tool | Version | Licence | Stars |
|---|---|---|---|
| gsttt | 0.1.196 | Apache-2.0 | 0 |
| CodeGraph | 1.6.2 | MIT | 77k |
| Graphify | 0.9.102 | Apache-2.0 | 142k |
| GitNexus | 1.6.12 | PolyForm Noncommercial | 48k |
I am the author of gsttt. No index build calls a model.
The checks are mechanical:
- typecheck of three packages
- the server and client test suites
- the GraphQL schema compared with the committed one
- nine held-out test files from the real change
- a 16-item checklist from the real diff, judged blind
The result
Cost is total_cost_usd at list prices, sub-agents included.
| Setup | Runs | Cost per run, USD | Mean |
|---|---|---|---|
| no harness, baselines | 3 | 10.72, 11.74, 11.84 | 11.43 |
| no harness, plugin only (four tools) | 4 | 11.75, 12.37, 11.25, 11.28 | 11.66 |
| harness, baselines | 5 | 6.84, 7.39, 7.83, 8.73, 9.28 | 8.01 |
| harness, gsttt | 2 | 9.44, 9.31 | 9.38 |
| harness, Graphify | 3 | 8.19, 10.29, 8.54 | 9.01 |
| harness, CodeGraph | 2 | 7.75, 9.55 | 8.65 |
| harness, GitNexus | 2 | 7.40, 9.72 | 8.56 |
!Dot chart of the cost per run in USD for no tool, gsttt, Graphify, CodeGraph and GitNexus, without and with my harness. In both panels the tool runs fall inside or next to the spread of the baselines.
The full-size chart
Quality did not move
Every run delivered the whole change:
- File recall against the real diff is 1.00 (0.97 once).
- The checklist is 16 of 16.
- The typechecks are clean and the GraphQL schema equals the real one.
The nine held-out tests failed in every run, because they pin names the original author chose. The unaided agent already solved the task, so a tool had nothing to add except savings.
The savings are inside the noise
The three bare baselines differ by 1.12 dollars, so a difference of about a dollar cannot be read. The harness baseline mean is 8.01.
| Tool | Runs | Difference from baseline mean |
|---|---|---|
| gsttt | 2 | +1.36 |
| Graphify | 3 | +1.00 |
| CodeGraph | 2 | +0.64 |
| GitNexus | 2 | +0.55 |
None is distinguishable from the baseline. The same tool run twice differs by 0.13 (gsttt) to 2.32 dollars (GitNexus). In the two big gaps, one run had one review round and the other had two.
gsttt has the highest mean, and both its runs had two review rounds. I wrote this tool, so weigh that.
Did the agents use the tools
Maybe the agents just ignored them. They did not. In every plugin-only run the agent used its tool unasked, because of what each installer writes into the project.
- Graphify adds a CLAUDE.md rule to query it before the first grep, two hooks and a 100 KB skill. The agent's first command was a graphify query.
- CodeGraph adds an MCP tool and a hook that injects 9,223 characters of context at the first prompt. The agent called codegraph_explore twice, each answer about 23k characters.
- GitNexus adds about 45 lines of MUST and NEVER rules, 12 skills and two hooks. The agent called impact 12 times, and the hook added a note to 10 of 90 Bash calls.
- gsttt writes nothing into the repository. The agent called impact three times.
The CodeGraph run was the most expensive of the twelve bare runs, $12.37. I cannot say what drove it.
The gsttt calls contradict my earlier 0 of 36 for installed-only gsttt. The model, the task and the plugin version differ, and one run per tool is not a rate.
So the tools were used, and the agent still did not need them.
What moves the bill
If the tools do not move the cost, something else does. The harness makes the same task $3.42 cheaper, 30%. My instructions send review and builder agents to Sonnet.
- In all 43 review launches of the 14 harness runs, the agent passed model: "sonnet". Without the harness, 34 of 40 ran on Opus.
- No experiment changed only the reviewer model, so this is a reading of the numbers.
The spread inside the harness is wider, 6.84 to 9.28 among the baselines. It follows the review rounds the agent runs:
| Review rounds | Baselines, mean USD | Tool runs, mean USD |
|---|---|---|
| one | 7.35 | 7.90 |
| two | 9.00 | 9.42 |
I do not know why the agent runs one round in some runs and two in others. It moves the cost by about two dollars, more than any tool did.
The moral
On paper, this looks great. A graph of how your code connects is a fun thing to build, and it is fun to look at.
Many people install tool after tool and believe that the more tools they add, the better their harness and their model get. I started from the same belief.
On this task, Claude did not care about these cool graphs of the codebase. It found every file without them. What moved the bill was boring: which model does the reviews and how many review rounds run.
So before you add the next tool to your harness, measure it on one real task of yours. Run the baseline at least three times first. If the number does not move, the tool is decoration.
Limits
- One task, one repository, one model at one effort. No statistical test.
- Recall and the checklist saturated at the maximum, and one AI agent judged the checklist. The typecheck, the tests and the schema equality carry the correctness evidence.
- Runs are few: the bare environment has 3 baselines and one run per tool arm, the harness 5 baselines, 2 runs per tool and 3 for Graphify.
- In round 1, ten claude.ai connectors were registered by mistake. No connector tool was called. Hooks of Graphify and GitNexus may have run twice in their round-1 enforced runs.
- The harness comes from one original session. I trust the direction, cheaper reviews and a wider spread, more than the exact figures.
- Wall-clock time is not comparable, because two runs shared the machine.
- I wrote one of the four tools and designed the test. The checks are mechanical, but weigh that.
- A task where the unaided agent misses files would be a fairer test of a code graph. This one did not have that problem.
If you want to reproduce it
1. Pick a merged change in your repository that spans a few packages.
2. Rebuild the parent commit as a single commit and write the ticket from the issue.
3. Run each setup with the same model and effort, in a folder the agent cannot leave, with your own setup loaded.
4. Install each tool as its README says, writing into the project and not into your global config.
5. Set CLAUDE_CODE_PRINT_BG_WAIT_CEILING_MS=0 for headless runs with sub-agents. By default the process stops background tasks 10 minutes after the last turn.
6. Run the baseline at least three times before trusting a difference.
The test runs, the blind judging and the first draft of this write-up were done by Claude Code agents from the run logs.
Mentions
I wrote a code-graph plugin for Claude Code and tested it against three popular ones on a real task. None of them helped. Every Claude Code session starts the same way. The agent does not know my codebase, so it greps, reads files and guesses what a change will break. I thought it would be great to give it a map. The agent asks one question, "what does this change affect?", and gets a short list of files and tests. The map is built from imports and from the contracts between packages: - HTTP routes - GraphQL operations - tables - queues - env variables So I built that tool and called it gsttt. A week later I had tables and charts in which it beat three popular tools. Then I tested all four on real work, and none of them made a measurable difference. This post is the whole story, from the idea to the moral. It includes the test setup, so you can check my work or redo it on your own repository. The short version - The idea: a map of the code should help an agent find what a change affects. - The tool: I built gsttt, and my own benchmark said it beat Graphify, GitNexus and codebase-memory-mcp. - The first reality check: on nine real commits, the installed tool was never called: 0 of 36 runs. - The last try: on one large real task, all four tools cost the same as no tool, inside the noise. - The moral: the agent did not need the graph. The bill followed which model does the code reviews and how many review rounds run. Contents - [The idea and the build](#the-idea-and-the-build) - [The first reality check](#the-first-reality-check) - [One last try with a real task](#one-last-try-with-a-real-task) - [The result](#the-result) - [Did the agents use the tools](#did-the-agents-use-the-tools) - [What moves the bill](#what-moves-the-bill) - [The moral](#the-moral) - [Limits](#limits) - [If you want to reproduce it](#if-you-want-to-reproduce-it) The idea and the build The code-graph tools I found did not treat contracts between packages as first-class edges. A GraphQL field in the server and the client query that uses it are an example. I wanted that link in the map, so I wrote my own on October 28. It is written in Rust, mostly with Claude Code. To see if it worked, I built a small multi-language fixture repository. I added fourteen questions with truth lists, such as "what depends on this file". Mean recall: | Tool | Mean recall | |---|---| | gsttt | 87% | | GitNexus | 73% | | codebase-memory-mcp | 72% | | Graphify | 46% | On Amplication, an open-source TypeScript monorepo, the TypeScript compiler also listed 97.3% of the callers gsttt found for 200 random methods. I was pleased. Then I looked at what the benchmark measured. I had written the fixture and the questions, knowing what gsttt does. A benchmark you write for your own tool measures whether it does what you built it to do. The first reality check So I ran the tool on real work: nine real merged changes, five from Amplication and four from a private monorepo. Each agent got a ticket written from the commit message, and I compared its diff with the real one. The model was Sonnet 5.5 at high effort, 99 runs in all. - Installed only: the agent never called gsttt. 0 of 36 runs, and 0 in 107 real sessions of mine. - With one sentence in the ticket ("Use gsttt to find what the change affects."), it called gsttt in all 27 runs. - The result: 4.4 more turns, about 4% more money, and no better recall of the real files: 0.64 against 0.66. I tried to fix that: - a skill text that says when to call the tool - answers in functions instead of files - cheaper answers - hints placed in the planning step and at stop time Nothing helped beyond the noise. An offline check on 420 commits compared gsttt with git co-change history. At the same list size, git history found 13 points more of the real files. For these agents, the graph carried no information they could not get from search. But those were small commits. A large change across many packages might be where a map pays off. One last try with a real task I also wanted the other tools in the picture. They have between 48,000 and 142,000 GitHub stars, and gsttt has 0. So I picked one large real task and ran all four tools on it. The task is a merged change in a private TypeScript monorepo for a SaaS product: an annual billing interval with a discount. It touches: - price calculation, the payment integration and the checkout service - a GraphQL contract on both sides, regenerated into three packages - a plan picker, a pricing section and translations in five locales - tests and an OpenSpec change proposal The real diff is 36 files, +1,183 and -96 lines. I chose it from 377 recorded sessions in that repository. It was the cheapest of four that edited at least eight files across three packages without any tool. The original session used Claude Opus 5.5 at xhigh effort. It took 57 turns and 15 sub-agents, cost about $8.10 and also made commits and a pull request. The codebase has six packages and 2,809 tracked files, about 282k lines of TypeScript. It carries its own agent setup, which stayed in every run: a CLAUDE.md, two sub-agent definitions and nine skills. The setup: - Each run starts in a fresh git repository with one commit, the parent state. The agent has no network and cannot read outside its folder. - The ticket is 335 words. Every run used Claude Opus 5.5 at xhigh effort, Claude Code 2.1.318, with a $15 cap. - Plugin only: the tool is installed as its README says, and nothing else. - Enforced (bare rounds only): the ticket also demands a tool call before the first edit and before the end. The gate never fired, because the agent called the tool first anyway. Two environments. The first one was a mistake. - Bare (rounds 1 and 2) loaded only the repository's own files. It is clean, but I never work that way, so its dollar figures do not describe my sessions. - Harness (round 3) also loads my own setup: my agorch plugin and superpowers, the two plugins of the original session, plus my instructions and rules. - In both, nothing else is loaded and nothing reaches outside the machine. Every run's start is audited against its arm. The tools are these four, at the versions I ran: | Tool | Version | Licence | Stars | |---|---|---|---| | gsttt | 0.1.196 | Apache-2.0 | 0 | | CodeGraph | 1.6.2 | MIT | 77k | | Graphify | 0.9.102 | Apache-2.0 | 142k | | GitNexus | 1.6.12 | PolyForm Noncommercial | 48k | I am the author of gsttt. No index build calls a model. The checks are mechanical: - typecheck of three packages - the server and client test suites - the GraphQL schema compared with the committed one - nine held-out test files from the real change - a 16-item checklist from the real diff, judged blind The result Cost is total_cost_usd at list prices, sub-agents included. | Setup | Runs | Cost per run, USD | Mean | |---|---|---|---| | no harness, baselines | 3 | 10.72, 11.74, 11.84 | 11.43 | | no harness, plugin only (four tools) | 4 | 11.75, 12.37, 11.25, 11.28 | 11.66 | | harness, baselines | 5 | 6.84, 7.39, 7.83, 8.73, 9.28 | 8.01 | | harness, gsttt | 2 | 9.44, 9.31 | 9.38 | | harness, Graphify | 3 | 8.19, 10.29, 8.54 | 9.01 | | harness, CodeGraph | 2 | 7.75, 9.55 | 8.65 | | harness, GitNexus | 2 | 7.40, 9.72 | 8.56 | !Dot chart of the cost per run in USD for no tool, gsttt, Graphify, CodeGraph and GitNexus, without and with my harness. In both panels the tool runs fall inside or next to the spread of the baselines. The full-size chart Quality did not move Every run delivered the whole change: - File recall against the real diff is 1.00 (0.97 once). - The checklist is 16 of 16. - The typechecks are clean and the GraphQL schema equals the real one. The nine held-out tests failed in every run, because they pin names the original author chose. The unaided agent already solved the task, so a tool had nothing to add except savings. The savings are inside the noise The three bare baselines differ by 1.12 dollars, so a difference of about a dollar cannot be read. The harness baseline mean is 8.01. | Tool | Runs | Difference from baseline mean | |---|---|---| | gsttt | 2 | +1.36 | | Graphify | 3 | +1.00 | | CodeGraph | 2 | +0.64 | | GitNexus | 2 | +0.55 | None is distinguishable from the baseline. The same tool run twice differs by 0.13 (gsttt) to 2.32 dollars (GitNexus). In the two big gaps, one run had one review round and the other had two. gsttt has the highest mean, and both its runs had two review rounds. I wrote this tool, so weigh that. Did the agents use the tools Maybe the agents just ignored them. They did not. In every plugin-only run the agent used its tool unasked, because of what each installer writes into the project. - Graphify adds a CLAUDE.md rule to query it before the first grep, two hooks and a 100 KB skill. The agent's first command was a graphify query. - CodeGraph adds an MCP tool and a hook that injects 9,223 characters of context at the first prompt. The agent called codegraph_explore twice, each answer about 23k characters. - GitNexus adds about 45 lines of MUST and NEVER rules, 12 skills and two hooks. The agent called impact 12 times, and the hook added a note to 10 of 90 Bash calls. - gsttt writes nothing into the repository. The agent called impact three times. The CodeGraph run was the most expensive of the twelve bare runs, $12.37. I cannot say what drove it. The gsttt calls contradict my earlier 0 of 36 for installed-only gsttt. The model, the task and the plugin version differ, and one run per tool is not a rate. So the tools were used, and the agent still did not need them. What moves the bill If the tools do not move the cost, something else does. The harness makes the same task $3.42 cheaper, 30%. My instructions send review and builder agents to Sonnet. - In all 43 review launches of the 14 harness runs, the agent passed model: "sonnet". Without the harness, 34 of 40 ran on Opus. - No experiment changed only the reviewer model, so this is a reading of the numbers. The spread inside the harness is wider, 6.84 to 9.28 among the baselines. It follows the review rounds the agent runs: | Review rounds | Baselines, mean USD | Tool runs, mean USD | |---|---|---| | one | 7.35 | 7.90 | | two | 9.00 | 9.42 | I do not know why the agent runs one round in some runs and two in others. It moves the cost by about two dollars, more than any tool did. The moral On paper, this looks great. A graph of how your code connects is a fun thing to build, and it is fun to look at. Many people install tool after tool and believe that the more tools they add, the better their harness and their model get. I started from the same belief. On this task, Claude did not care about these cool graphs of the codebase. It found every file without them. What moved the bill was boring: which model does the reviews and how many review rounds run. So before you add the next tool to your harness, measure it on one real task of yours. Run the baseline at least three times first. If the number does not move, the tool is decoration. Limits - One task, one repository, one model at one effort. No statistical test. - Recall and the checklist saturated at the maximum, and one AI agent judged the checklist. The typecheck, the tests and the schema equality carry the correctness evidence. - Runs are few: the bare environment has 3 baselines and one run per tool arm, the harness 5 baselines, 2 runs per tool and 3 for Graphify. - In round 1, ten claude.ai connectors were registered by mistake. No connector tool was called. Hooks of Graphify and GitNexus may have run twice in their round-1 enforced runs. - The harness comes from one original session. I trust the direction, cheaper reviews and a wider spread, more than the exact figures. - Wall-clock time is not comparable, because two runs shared the machine. - I wrote one of the four tools and designed the test. The checks are mechanical, but weigh that. - A task where the unaided agent misses files would be a fairer test of a code graph. This one did not have that problem. If you want to reproduce it 1. Pick a merged change in your repository that spans a few packages. 2. Rebuild the parent commit as a single commit and write the ticket from the issue. 3. Run each setup with the same model and effort, in a folder the agent cannot leave, with your own setup loaded. 4. Install each tool as its README says, writing into the project and not into your global config. 5. Set CLAUDE_CODE_PRINT_BG_WAIT_CEILING_MS=0 for headless runs with sub-agents. By default the process stops background tasks 10 minutes after the last turn. 6. Run the baseline at least three times before trusting a difference. The test runs, the blind judging and the first draft of this write-up were done by Claude Code agents from the run logs.
Stop AI Coding Blind Spots with GitNexus Cursor and Claude hallucinate because they lack full repository context. Enter GitNexus. In two CLI commands, it maps your entire codebase into a local knowledge graph of dependencies and call chains. Zero servers required. It exposes this architecture directly to your AI via MCP tools. Now, your AI actually understands your execution flows. This drastically improves reliability and stops broken imports. Repo: abhigyanpatwari/gitnexus #Shorts #GitHub #OpenSource #Coding
Top 10 AI GitHub Repos RIGHT NOW — October 18, 2026 #Shorts Today's fastest-rising AI repositories on GitHub, ranked by real momentum from the last 24-48 hours rather than all-time size. Every repository was checked against the GitHub API on 18 October 2026, and the list is dominated by agent infrastructure: orchestration runtimes, control planes, memory layers, security skills and fast decision engines. Ranked list (the video counts down 10 → 1): 1. mattpocock/skills — mattpocock/skills 2. DietrichGebert/ponytail — DietrichGebert/ponytail 3. Egonex-AI/Understand-Anything — Egonex-AI/Understand-Anything 4. debpalash/VoiceStudio — debpalash/VoiceStudio 5. HKUDS/NanoBot — HKUDS/NanoBot 6. vectorize-io/hindsight — vectorize-io/hindsight 7. abhigyanpatwari/GitNexus — abhigyanpatwari/GitNexus 8. google-ai-edge/gallery — google-ai-edge/gallery 9. NVIDIA/SkillSpector — NVIDIA/SkillSpector 10. rocketride-org/rocketride-server — rocketride-org/rocketride-server Disclaimer: rankings are based on GitHub trending and star-velocity snapshots for 18 October 2026 and change daily. Star counts are approximate and move fast — they were read from the GitHub API on the day of recording. Licences are mentioned only where a repository clearly states one. New AI GitHub Top 10 every day — subscribe for tomorrow's list. #GitHub #AI #Agents #OpenSource #LLM #MachineLearning --- More AI content --- ⚡ AI in 60 Seconds: youtube.com 🤖 Hermes Agent News: youtube.com 🔬 Top 10 HF Models: youtube.com 💬 Join our Discord community: discord.gg
Top 10 AI GitHub Repos RIGHT NOW — October 18, 2026 Today's fastest-rising AI repositories on GitHub, ranked by real momentum from the last 24-48 hours rather than all-time size. Every repository was checked against the GitHub API on 18 October 2026, and the list is dominated by agent infrastructure: orchestration runtimes, control planes, memory layers, security skills and fast decision engines. Ranked list (the video counts down 10 → 1): 1. mattpocock/skills — mattpocock/skills 2. DietrichGebert/ponytail — DietrichGebert/ponytail 3. Egonex-AI/Understand-Anything — Egonex-AI/Understand-Anything 4. debpalash/VoiceStudio — debpalash/VoiceStudio 5. HKUDS/NanoBot — HKUDS/NanoBot 6. vectorize-io/hindsight — vectorize-io/hindsight 7. abhigyanpatwari/GitNexus — abhigyanpatwari/GitNexus 8. google-ai-edge/gallery — google-ai-edge/gallery 9. NVIDIA/SkillSpector — NVIDIA/SkillSpector 10. rocketride-org/rocketride-server — rocketride-org/rocketride-server Disclaimer: rankings are based on GitHub trending and star-velocity snapshots for 18 October 2026 and change daily. Star counts are approximate and move fast — they were read from the GitHub API on the day of recording. Licences are mentioned only where a repository clearly states one. CHAPTERS 0:00 Hook 0:09 Intro 0:24 #10 rocketride-org/rocketride-server 1:00 #9 NVIDIA/SkillSpector 1:35 #8 google-ai-edge/gallery 2:08 #7 abhigyanpatwari/GitNexus 2:34 #6 vectorize-io/hindsight 3:09 #5 HKUDS/NanoBot 3:44 #4 debpalash/VoiceStudio 4:14 #3 Egonex-AI/Understand-Anything 4:46 #2 DietrichGebert/ponytail 5:24 #1 mattpocock/skills 6:00 Pattern of the day 6:21 CTA + outro New AI GitHub Top 10 every day — subscribe for tomorrow's list. #GitHub #AI #Agents #OpenSource #LLM #MachineLearning --- More AI content --- ⚡ AI in 60 Seconds: youtube.com 🤖 Hermes Agent News: youtube.com 🔬 Top 10 HF Models: youtube.com 💬 Join our Discord community: discord.gg
@irohemmi iyi görünüyor, geçen gün bu konuyla alakalı önüme şu araç düşmüştü 50k starı varmış yeni öğrendim :D abhigyanpatwari/GitNexus
Top 10 AI GitHub Repos RIGHT NOW — October 17, 2026 #Shorts Today's fastest-rising AI repositories on GitHub, ranked by real momentum from the last 24-48 hours rather than all-time size. Every repository was checked against the GitHub API on 17 October 2026, and the list is dominated by agent infrastructure: orchestration runtimes, control planes, memory layers, security skills and fast decision engines. Ranked list (the video counts down 10 → 1): 1. mattpocock/skills — mattpocock/skills 2. DietrichGebert/ponytail — DietrichGebert/ponytail 3. Egonex-AI/Understand-Anything — Egonex-AI/Understand-Anything 4. debpalash/VoiceStudio — debpalash/VoiceStudio 5. HKUDS/NanoBot — HKUDS/NanoBot 6. vectorize-io/hindsight — vectorize-io/hindsight 7. abhigyanpatwari/GitNexus — abhigyanpatwari/GitNexus 8. google-ai-edge/gallery — google-ai-edge/gallery 9. NVIDIA/SkillSpector — NVIDIA/SkillSpector 10. rocketride-org/rocketride-server — rocketride-org/rocketride-server Disclaimer: rankings are based on GitHub trending and star-velocity snapshots for 17 October 2026 and change daily. Star counts are approximate and move fast — they were read from the GitHub API on the day of recording. Licences are mentioned only where a repository clearly states one. New AI GitHub Top 10 every day — subscribe for tomorrow's list. #GitHub #AI #Agents #OpenSource #LLM #MachineLearning --- More AI content --- ⚡ AI in 60 Seconds: youtube.com 🤖 Hermes Agent News: youtube.com 🔬 Top 10 HF Models: youtube.com 💬 Join our Discord community: discord.gg
Top 10 AI GitHub Repos RIGHT NOW — October 15, 2026 Today's fastest-rising AI repositories on GitHub, ranked by real momentum from the last 24-48 hours rather than all-time size. Every repository was checked against the GitHub API on 15 October 2026, and the list is dominated by agent infrastructure: orchestration runtimes, control planes, memory layers, security skills and fast decision engines. Ranked list (the video counts down 10 → 1): 1. mattpocock/skills — mattpocock/skills 2. DietrichGebert/ponytail — DietrichGebert/ponytail 3. Egonex-AI/Understand-Anything — Egonex-AI/Understand-Anything 4. debpalash/VoiceStudio — debpalash/VoiceStudio 5. HKUDS/NanoBot — HKUDS/NanoBot 6. vectorize-io/hindsight — vectorize-io/hindsight 7. abhigyanpatwari/GitNexus — abhigyanpatwari/GitNexus 8. google-ai-edge/gallery — google-ai-edge/gallery 9. NVIDIA/SkillSpector — NVIDIA/SkillSpector 10. rocketride-org/rocketride-server — rocketride-org/rocketride-server Disclaimer: rankings are based on GitHub trending and star-velocity snapshots for 15 October 2026 and change daily. Star counts are approximate and move fast — they were read from the GitHub API on the day of recording. Licences are mentioned only where a repository clearly states one. CHAPTERS 0:00 Hook 0:09 Intro 0:24 #10 rocketride-org/rocketride-server 0:58 #9 NVIDIA/SkillSpector 1:32 #8 google-ai-edge/gallery 2:02 #7 abhigyanpatwari/GitNexus 2:29 #6 vectorize-io/hindsight 3:01 #5 HKUDS/NanoBot 3:36 #4 debpalash/VoiceStudio 4:04 #3 Egonex-AI/Understand-Anything 4:34 #2 DietrichGebert/ponytail 5:11 #1 mattpocock/skills 5:51 Pattern of the day 6:14 CTA + outro New AI GitHub Top 10 every day — subscribe for tomorrow's list. #GitHub #AI #Agents #OpenSource #LLM #MachineLearning --- More AI content --- ⚡ AI in 60 Seconds: youtube.com 🤖 Hermes Agent News: youtube.com 🔬 Top 10 HF Models: youtube.com 💬 Join our Discord community: discord.gg