GitHub Trending Developers is often more interesting than GitHub Trending Repos. Repos tell you what is hot. Developers tell you who is repeatedly finding the edges: background agents, research skills, local inference, agent observability, creative skills, dashboards, and open-source utilities.
This snapshot is especially agent-heavy. The most useful pattern is not "follow famous people." It is this: look at the builders who are turning AI work into systems with control planes, skill files, memory, logs, dashboards, and reusable workflows.
Source Note
This post is based on the supplied GitHub Trending Developers snapshot and a live check of the page on June 22, 2026. GitHub's developer trending page changes quickly, so treat the order as a time-sensitive signal, not a permanent ranking.
For each developer, I included the GitHub profile link, the popular repo link when one was shown, and the practical skill or pattern I would study from that person.
What The List Shows
The list splits into six useful clusters:
- Agent control planes: Cole Murray, Q00, rUv, ogul-style meta-harness builders, and related agent OS work.
- Research and knowledge work: Matt Van Horn, Luis Novo, Owain Lewis, xiaolai.
- Observability and runtime tooling: Jarrod Watts, Neco, Clement Tsang, Daniel Kraus.
- Local and infrastructure systems: Eric Buehler, Matt Kane, Lee Calcote, bdraco.
- Creative AI workflows: Anion, Trevin Chow, and presentation/illustration skill builders.
- Applied dashboards and hardware: ZhuLinsen, Elie Habib, Daniel Oster, AutoJanitor.
Trending Developers To Study
The table below is intentionally practical. The goal is not fan worship. The goal is to learn what each builder is good at and decide whether their repo or style belongs in your own workflow.
| # | Developer | Popular repo | Best skill to study | JQ AI SYSTEMS read |
|---|---|---|---|---|
| 1 |
Cole Murray
@ColeMurray |
background-agents
Open-source background agents coding system. |
Background-agent architecture, control planes, sandboxed execution, and async coding workflows. | Study Cole if you want to move from chat-based coding to task queues and background agents. |
| 2 |
mumu
@ZhuLinsen |
daily_stock_analysis
LLM-powered multi-market stock analysis with market data, news, dashboards, and notifications. |
Data ingestion, decision dashboards, scheduled analysis, and financial workflow packaging. | Good for learning how to turn data feeds into a recurring analysis product. Not financial advice. |
| 3 |
Matt Van Horn
@mvanhorn |
last30days-skill
Research skill for synthesizing recent signals across Reddit, X, YouTube, Hacker News, Polymarket, and the web. |
Recency-aware research, source synthesis, agent skills, and grounded briefing workflows. | Follow for the research-skill pattern: make the agent gather recent context before it writes. |
| 4 |
Luis Novo
@lfnovo |
open-notebook
Open-source NotebookLM-style research workspace with more flexibility and model control. |
Knowledge work, private research spaces, multimodal source organization, and model-flexible notebooks. | A good person to study if you want research workflows that are not locked into one vendor. |
| 5 |
Fayner Brack
@FagnerMartinsBrack |
Profile only in supplied snapshot
No popular repo was shown in the supplied snapshot. |
Independent web engineering, maintainability, and public technical presence. | Treat this as a profile to inspect rather than a repo pick. |
| 6 |
Q00
@Q00 |
ouroboros
Agent OS: Stop prompting. Start specifying. |
Specification-first agent work, replayable execution, observability, and evaluation loops. | One of the most important patterns here: stop giving vague prompts and start specifying contracts. |
| 7 |
Clement Tsang
@ClementTsang |
bottom
Cross-platform graphical process and system monitor. |
Rust CLI/TUI engineering, performance interfaces, and developer observability. | Useful reminder that agent builders still need clean system visibility. |
| 8 |
J. Nick Koston
@bdraco |
Profile only in supplied snapshot
No popular repo was shown in the supplied snapshot. |
Automation reliability, Python systems, and long-term maintainer discipline. | Study for operational engineering habits rather than a single repo. |
| 9 |
Daniel Kraus
@dakra |
ghostel
Terminal emulator powered by libghostty. |
Terminal UX, systems tooling, and developer-environment polish. | Interesting because so much AI-agent work still happens in terminal surfaces. |
| 10 |
Elie Habib
@koala73 |
worldmonitor
Real-time global intelligence dashboard for news, geopolitics, and infrastructure monitoring. |
AI dashboards, situational awareness, source aggregation, and monitoring UX. | A good model for research dashboards, but source quality and noise control are the product. |
| 11 |
Nik Shevchenko
@kodjima33 |
Profile only in supplied snapshot
The supplied snapshot notes work for omi, but no popular repo entry. |
AI product engineering, wearable/assistant product direction, and shipping public-facing tools. | Useful profile to inspect if you track consumer AI hardware and assistant products. |
| 12 |
Anion
@Anionex |
banana-slides
AI-native presentation generator built around Nano Banana Pro-style workflows. |
AI slide generation, editable deck export, template parsing, and multimodal presentation workflows. | Good to study for "Vibe PPT": turning outlines and assets into editable business decks. |
| 13 |
AutoJanitor
@Scottcjn |
Rustchain
DePIN / Proof-of-Antiquity blockchain concept for vintage hardware. |
Rust systems experiments, hardware fingerprinting ideas, and niche protocol design. | Fun and strange, but treat it as experimental until proved otherwise. |
| 14 |
Trevin Chow
@tmchow |
illo-skill
Agent skill for turning ideas and articles into editorial illustrations. |
Creative production skills, illustration direction, repeatable character systems, and article-to-image workflows. | A strong example of skills becoming creative-production machinery, not just coding helpers. |
| 15 |
Jarrod Watts
@jarrodwatts |
claude-hud
Claude Code plugin showing context usage, active tools, running agents, and todo progress. |
Agent observability, Claude Code UX, context monitoring, and tool transparency. | Excellent builder to follow if you want to see what your coding agent is actually doing. |
| 16 |
Eric Buehler
@EricLBuehler |
mistral.rs
Fast, flexible LLM inference. |
Local inference, Rust performance, multimodal model serving, and tool-aware model infrastructure. | Important for anyone building local-first or cost-controlled AI systems. |
| 17 |
Neco
@Necmttn |
ax
Agent experience layer with observability and memory for Claude Code and Codex. |
Local-first observability, agent memory, typed developer experience, and AI coding telemetry. | Another strong signal that agent work needs logs, memory, and visibility. |
| 18 |
AmirHossein Abdolmotallebi
@amir1376 |
ab-download-manager
Download manager that speeds up downloads. |
Cross-platform desktop utilities, reliability, and user-facing tool polish. | A useful non-AI reminder: boring utilities still win when they solve a clear pain. |
| 19 |
Owain Lewis
@owainlewis |
awesome-artificial-intelligence
Curated list of AI courses, books, lectures, and papers. |
Curation, learning maps, and knowledge organization. | Good source if you want to go deeper than tool demos and understand the field. |
| 20 |
Matt Kane
@ascorbic |
cirrus
Single-user ATProto PDS running on a Cloudflare Worker. |
Edge deployment, protocol implementation, and lightweight personal infrastructure. | Worth studying if you care about small, personal internet infrastructure. |
| 21 |
ManlyMarco
@ManlyMarco |
RuntimeUnityEditor
In-game inspector and debugging tools for Unity3D applications. |
Runtime inspection, game/tool debugging, and developer tooling inside complex apps. | Good reminder that great tooling often lives inside the runtime, not outside it. |
| 22 |
Lee Calcote
@leecalcote |
Profile only in supplied snapshot
The supplied snapshot notes work for Layer5, but no popular repo entry. |
Cloud-native systems, open-source community, and infrastructure education. | Follow for cloud-native operating-model lessons, especially if your agents need infrastructure. |
| 23 |
Daniel Oster
@dalathegreat |
Battery-Emulator
Software for reusing EV battery packs for stationary storage with solar inverters. |
Hardware reuse, energy systems, embedded workflows, and practical sustainability engineering. | One of the best non-AI examples here: real-world engineering beats hype. |
| 24 |
xiaolai
@xiaolai |
too-late
Public fiction project. |
Long-form writing, public publishing, and using GitHub as a creative archive. | Interesting because GitHub is not only software now. It is also public knowledge and publishing infrastructure. |
| 25 |
rUv
@ruvnet |
ruflo
Agent meta-harness for Claude with multi-agent swarms and workflow coordination. |
Multi-agent orchestration, agent swarms, adaptive memory, RAG integration, and Claude/Codex integration. | Follow if you want to understand where agent orchestration may go after single-agent coding. |
Related Videos
I found a few related videos that help explain the patterns behind the list. Treat them as context, not as official documentation for every repo.
Cole Murray on internal background agent systems. Useful context for why background coding agents need more than a chat box.
Open Notebook overview. Shows the open-source NotebookLM-style direction behind Luis Novo's work.
claude-hud development visualization. Related context for Jarrod Watts' Claude HUD plugin and agent visibility tooling.
Meta-harness direction for coding agents. Useful background for the broader ruflo / agent-harness pattern.
JQ AI SYSTEMS Top 10 Developers To Watch
If I had to reduce the list to ten developers to study this week, this is my practical ranking for agent builders and technical operators.
- Cole Murray: Best background-agent architecture signal. Study the move from chat sessions to task queues and control planes.
- Q00: Best specification-first agent pattern. Ouroboros is the sharpest "stop prompting, start specifying" idea on the list.
- Jarrod Watts: Best observability pick. Claude HUD solves a real pain: knowing what the agent is doing.
- Matt Van Horn: Best research-skill pick. last30days-skill is a strong model for source-grounded recent research.
- Luis Novo: Best knowledge-work pick. Open Notebook shows where private, flexible research spaces are going.
- Eric Buehler: Best local-inference pick. mistral.rs matters for builders who want local, fast, flexible model serving.
- Neco: Best agent-experience pick. ax points toward typed, local-first memory and observability for Codex and Claude Code.
- rUv: Best multi-agent orchestration pick. Ruflo is worth watching if you are exploring swarms and agent coordination.
- Elie Habib: Best intelligence-dashboard pick. World Monitor shows demand for AI-assisted situational awareness.
- Trevin Chow: Best creative-skill pick. illo-skill shows how agent skills can become repeatable visual production systems.
My top three are Cole Murray, Q00, and Jarrod Watts. Cole represents background agents. Q00 represents specification-first agent work. Jarrod represents observability. Those three ideas together are a serious agent architecture: queue the work, specify the work, and watch the work.
Skills To Learn From This Group
- Background execution: Do not keep every AI task trapped in a chat thread. Study background-agents.
- Specification over prompting: Vague prompts do not scale. Study Ouroboros and spec-first agent loops.
- Agent visibility: You cannot manage what you cannot see. Study Claude HUD and ax.
- Recent research as a skill: Build reusable research skills that keep source links attached.
- Local inference: If cost, privacy, or resilience matters, study mistral.rs and local model serving.
- Creative skill packaging: illo-skill and banana-slides show skills moving into design, slides, and illustration.
- Operational dashboards: worldmonitor and daily_stock_analysis show how AI becomes useful when it is attached to feeds, alerts, and decisions.
Builder Checklist
- Follow people by layer. Pick three: one agent builder, one infrastructure builder, one workflow/skill builder.
- Clone only after reading. Look at README, issues, recent commits, install steps, and permission requirements first.
- Map the reusable pattern. Ask what the developer is really good at: memory, observability, UI, inference, dashboards, creative skills, or specs.
- Do not copy blindly. A repo can be hot and still wrong for your stack.
- Build a tiny test. If a developer's pattern is useful, make a 30-minute local experiment before adding it to your workflow.
- Turn good patterns into your own skill. The durable win is not a star. It is a reusable workflow you can run again.
The CTA: do not just follow trending developers. Reverse-engineer what they are good at, then add one useful pattern to your own agent stack this week.
Sources
- GitHub Trending Developers, checked June 22, 2026.
- @ColeMurray - background-agents
- @ZhuLinsen - daily_stock_analysis
- @mvanhorn - last30days-skill
- @lfnovo - open-notebook
- @FagnerMartinsBrack
- @Q00 - ouroboros
- @ClementTsang - bottom
- @bdraco
- @dakra - ghostel
- @koala73 - worldmonitor
- @kodjima33
- @Anionex - banana-slides
- @Scottcjn - Rustchain
- @tmchow - illo-skill
- @jarrodwatts - claude-hud
- @EricLBuehler - mistral.rs
- @Necmttn - ax
- @amir1376 - ab-download-manager
- @owainlewis - awesome-artificial-intelligence
- @ascorbic - cirrus
- @ManlyMarco - RuntimeUnityEditor
- @leecalcote
- @dalathegreat - Battery-Emulator
- @xiaolai - too-late
- @ruvnet - ruflo
- YouTube: Cole Murray on internal background agent systems
- YouTube: Open Notebook overview
- YouTube: claude-hud development visualization
- YouTube: Meta-harness direction for coding agents