💡 jundot/omlx just hit the trending charts — here's why it matters.

🔗 https://github.com/jundot/omlx
📝 LLM inference server with continuous batching & SSD caching for Apple Silicon — managed from the macOS menu bar
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🧠 Channel: https://t.me/GithubRe

🔥 genlayerlabs/genlayer-project-boilerplate is trending — and it deserves your attention.

🔗 https://github.com/genlayerlabs/genlayer-project-boilerplate
📝 No description.
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🧠 Channel: https://t.me/GithubRe

🎯 OpenCut-app/OpenCut landed on trending. Worth a proper look.

🔗 https://github.com/OpenCut-app/OpenCut
📝 The open-source CapCut alternative
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🧠 Channel: https://t.me/GithubRe

📌 Spotted on GitHub Trending: harry0703/MoneyPrinterTurbo — let's break it down.

🔗 https://github.com/harry0703/MoneyPrinterTurbo
📝 利用 AI 大模型和自动化工作流，根据主题或关键词一键生成高清短视频。Generate HD short videos from a topic or keyword with an automated AI workflow.
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MoneyPrinterTurbo 💸 – One‑stop AI short‑video generator

Turn a simple topic or keyword into a polished, high‑definition short video – script, footage, subtitles, voice‑over and music all done automatically.

---

Why it matters
If you ever needed a quick TikTok, Instagram Reel or YouTube Short but lacked time or editing skills, MoneyPrinterTurbo does the heavy lifting. Feed it a phrase, pick a format, and the system produces a ready‑to‑publish video in seconds.

---

Key features

- Four interaction modes: AI Agent, Web UI, REST API, and CLI – pick the style that fits your workflow.
- AI‑generated script (or you can supply your own).
- Multiple HD resolutions:
• Portrait 9:16 (1080×1920)
• Landscape 16:9 (1920×1080)
- Batch creation: generate many variants at once and select the best.
- Adjustable clip length for fine‑tuned pacing.
- Multilingual script support.
- Voice synthesis from a wide range of providers (Edge TTS, Azure Speech, SiliconFlow, Google Gemini, Xiaomi MiMo, ElevenLabs, Chatterbox) with real‑time preview.
- Customizable subtitles: font, size, color, border, background, position.
- Background music: random pick or user‑provided track, volume control.
- Asset sourcing: use your local media or pull royalty‑free clips from Pexels, Pixabay, Coverr.
- Model agnostic: works with Kimi (Moonshot), OpenAI, Google Gemini, DeepSeek, Alibaba Tongyi Qianwen, Azure OpenAI, Volcengine Ark, xAI Grok, MiniMax, and many more through Cloudflare AI Gateway, ModelScope, Ollama, LiteLLM, Groq, etc.
- One‑click cross‑platform publishing: auto‑upload to TikTok, Instagram, YouTube Shorts.

---

Typical usage

Web UI – launch the server, open a browser, type a topic, hit “Generate”, and watch the video assemble in the preview pane.

CLI example

python -m moneyprinterturbo \\
    --topic "Future of renewable energy" \\
    --resolution portrait \\
    --language zh \\
    --tts elevenlabs \\
    --output ./output/video.mp4

API call (JSON payload)

{
  "topic": "Space exploration in 2050",
  "resolution": "landscape",
  "language": "en",
  "tts_provider": "azure",
  "music": "random",
  "publish": ["tiktok", "youtube"]
}

The system then:
1. Uses the selected LLM to draft a concise script.
2. Extracts key visual keywords and searches the chosen stock libraries.
3. Generates voice‑over, syncs subtitles, mixes background music.
4. Renders the final video file and optionally pushes it to the selected platforms.

---

Technical highlights

- Modular architecture: controllers, services, and model adapters are cleanly separated, making extensions straightforward.
- Unified model gateway: a thin abstraction layer translates calls to any supported LLM or multimodal model, so you can swap providers without code changes.
- Asynchronous pipeline built on Python 3.11+ asyncio, allowing parallel downloading of assets and concurrent TTS synthesis for fast turnaround.
- Docker‑friendly: a single‑command container image is provided for hassle‑free deployment on Windows, macOS or Linux.
- Extensible asset plugins let you add new stock‑video APIs or point to private media collections.

---

Who should try it
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- Content creators and marketers looking to scale short‑form video output.
- Small businesses that need affordable, automated promo clips.
- Developers who want to embed AI video generation into their own products via the API.
- Educators or hobbyists experimenting with AI‑driven multimedia pipelines.

---

Get started
1. git clone https://github.com/harry0703/MoneyPrinterTurbo.git
2. cd MoneyPrinterTurbo && pip install -r requirements.txt
3. Choose a mode (Web UI: python -m moneyprinterturbo.webui, CLI, or API) and follow the on‑screen prompts.

---

Takeaway – MoneyPrinterTurbo turns a single idea into a scroll‑stopping video, all without lifting a finger.

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📌 Spotted on GitHub Trending: modular/modular — let's break it down.

🔗 https://github.com/modular/modular
📝 The Modular Platform (includes MAX & Mojo)
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What is Modular?
Modular is an open‑source platform that brings together everything you need to build, train, and serve AI models. It ships two star components: the MAX Framework 🧑‍🚀 for high‑performance inference, and the Mojo language 🔥 for fast, low‑level model code.

Key pieces you’ll find in this repo

Mojo compiler – the /KGEN folder contains the compiler front‑end.
Mojo standard library – ready‑to‑use utilities live in /mojo/stdlib.
MAX accelerator library – GPU/TPU kernels are under /max/kernels.
MAX inference server – an OpenAI‑compatible endpoint in /max/python/max/serve.
MAX model pipelines – Python‑based graph pipelines in /max/python/max/pipelines.
Examples – real‑world demos for both MAX and Mojo in /max/examples and /mojo/examples.


Getting started in a nutshell
If you just want to spin up a model with MAX, follow the official quick‑start:

# Clone the repo
git clone https://github.com/modular/modular.git
cd modular

# Install the Python side
pip install -r max/python/requirements.txt

# Run the example server
python -m max.serve --model your_model_name

For Mojo, the quick‑start guide walks you through installing the compiler and running a hello‑world program:

# Install Mojo (see the Mojo docs for the latest command)
curl -sSf https://install.mojo-lang.org | bash

# Compile and run a Mojo file
mojo my_program.mojo

Technical highlights

Unified code base – Both the accelerator kernels and the inference server are written in Python, while performance‑critical parts live in Mojo, letting you drop to native speed when needed.
OpenAI‑compatible API – The MAX server speaks the same JSON schema as OpenAI, so existing client libraries work out‑of‑the‑box.
Modular pipelines – Graph‑style pipelines let you compose preprocessing, model execution, and post‑processing with just a few Python lines.
Extensible standard library – Mojo’s stdlib is open for contributions, so you can add new data structures or math helpers without waiting for a new compiler release.
Apache 2.0 + LLVM exceptions – Most of the repo is permissively licensed; the MAX components follow the Modular Community License.


Who should dive in?

AI researchers who need a fast inference server that can be swapped into existing pipelines.
Systems engineers looking to write custom kernels in a language that compiles to native code.
Python developers who want to experiment with AI models but also need the option to drop into Mojo for speed‑critical sections.
Open‑source contributors eager to shape the future of a unified AI platform.


Community & support
Join the conversation on Discord, the forum, or the regular community calls. All events and recordings are posted on the Meetup page and YouTube channel.

Takeaway – Modular gives you the freedom to prototype in Python and accelerate to native performance with Mojo, all under one open‑source roof.

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* **Engineers who already use AI assistants** and want reproducible, low‑risk workflows.
* **Team leads** needing a shared language and ticket‑generation pipeline that the whole squad (human and AI) can follow.
* **Solo developers** who want the safety net of TDD and design reviews without writing boilerplate scripts.
* **Tool‑builders** looking for a ready‑made catalog of prompt‑driven capabilities to embed in their own agents.

Bottom line

These skills turn a clever LLM into a disciplined teammate, letting you keep the creative speed of AI while preserving the rigor of real software engineering.

Takeaway: Give your AI a playbook, and it will stop guessing and start building.

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🧠 Channel: https://t.me/GithubRe
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🔍 Deep-diving into AprilNEA/OpenLogi — fresh off the trending list.

🔗 https://github.com/AprilNEA/OpenLogi
📝 ⚡️A native, local-first alternative to Logitech Options+, written in Rust 🦀 — remap buttons, DPI, and SmartShift over HID++. No account, no telemetry.
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OpenLogi – a native, local‑first replacement for Logitech Options+

What it is
OpenLogi is a Rust‑powered application that lets you unlock the full HID++ and UVC capabilities of Logitech mice, keyboards, and webcams. It runs locally on macOS, Linux and Windows, giving you deep control without any cloud‑side account.

Why you might care
If you’re fed up with Logitech’s Options+ (heavy, Windows‑only, closed source) or you need a truly cross‑platform solution, OpenLogi offers a lightweight alternative that works on Linux out of the box, lets you script everything via a CLI, and stores all settings in a single, human‑readable TOML file.

Key features at a glance

• Universal device support – Logi Bolt, Unifying receivers, Bluetooth or wired connections, with live battery status.
• Button remapping – OS‑level input hook, built‑in action catalog, plus custom shortcuts defined in TOML.
• Per‑application profiles – automatic overlay switching when a window gains focus (macOS + Windows; Linux on X11/XWayland).
• Gestures on any button – assign directional gestures to any physical button or disable them entirely.
• Actions Ring – an eight‑slot, cursor‑centered overlay for quick actions, customizable per app.
• DPI & SmartShift – preset DPI cycles, smart wheel modes and permanent ratchet panels.
• Keyboard RGB – static lighting control on supported models.
• Webcam control – plug‑and‑play UVC cameras (Brio, StreamCam, C920, …) with live preview, hardware‑level image settings (zoom, focus, exposure, white‑balance, etc.) and one‑click profiles (Default / Streaming / Video call).
• Litra lights – power, brightness and colour‑temperature control, with optional auto‑power tied to camera activity.

Technical highlights

- Written in Rust with the GPUI toolkit for a responsive native UI.
- Uses a dedicated background openlogi-agent that owns HID++ I/O, exposing a clean CLI for automation.
- Linux packages install udev rules so the agent can access /dev/hidraw*, /dev/uinput and input events without sudo.
- Configuration lives in a single config.toml file – easy to version‑control and sync across machines.
- Media‑key actions on Linux go through D‑Bus MPRIS; macOS/Windows map to native equivalents where possible.
- Supports both x86_64/amd64 and arm64/aarch64 binaries; NixOS module available for declarative installs.

Who it’s for

- Power users who want granular control over every Logitech peripheral.
- Developers and sysadmins who prefer scriptable, file‑based configs.
- Linux enthusiasts who have been waiting for a first‑class Logitech manager.
- Anyone who wants a stable, open‑source alternative to Options+ without sacrificing features.

Getting started (quick install)

macOS – download the notarized .dmg from the latest release or install via Homebrew:

brew install --cask openlogi

Linux – pick the package for your distro:

# Debian/Ubuntu
sudo dpkg -i openlogi_*.deb

# Fedora/RHEL
sudo rpm -i openlogi-*.rpm

# Arch
sudo pacman -U openlogi-*.pkg.tar.zst

After installation, enable the agent for your user:

systemctl --user enable --now openlogi-agent.service

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Windows – download the signed .msi or portable .zip, unzip side‑by‑side with openlogi-agent.exe, and run OpenLogi.exe. The agent lives in the system tray.

Using the CLI – all actions are also available from the command line; see USAGE.md for the full list. Example to list connected devices:

openlogi list-devices

Configuration basics – edit the single config.toml (usually under ~/.config/openlogi/) to define profiles, button mappings, gestures, and webcam presets. Changes are applied instantly; no restart required.

Community & development

- Active development; features and config may evolve.
- Contributions welcome – see DEVELOPMENT.md for build instructions.
- Thanks to contributors for Linux, Windows, camera support, and the underlying HID++ library (fork of hidpp).

License – dual‑licensed under Apache 2.0 or MIT, giving you flexibility for commercial or open‑source use.

Takeaway – OpenLogi puts the power of Logitech hardware back in your hands, cross‑platform and fully scriptable.

Turn your Logitech gear into a truly personal workspace.

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🧠 Channel: https://t.me/GithubRe
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🌟 obra/superpowers caught my eye on GitHub Trending today.

🔗 https://github.com/obra/superpowers
📝 An agentic skills framework & software development methodology that works.
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Superpowers is a full‑stack methodology that turns AI‑powered coding agents into disciplined, test‑driven engineers. It works by loading a library of composable skills that automatically guide the agent through every stage of software creation – from brainstorming a spec to delivering a clean, merge‑ready branch.

Why it matters
Instead of letting an LLM dive straight into code, Superpowers makes the agent pause, ask clarifying questions, and produce a readable design document. Once you approve the spec, the agent builds a granular implementation plan, then executes it with sub‑agent‑driven development, two‑stage reviews, and strict RED‑GREEN‑REFACTOR cycles. The result is code that respects TDD, YAGNI and DRY without you having to micromanage the bot.

Key Features


Skills Library – over a dozen ready‑to‑use skills covering testing, debugging, collaboration and meta‑tasks (e.g. test-driven-development, systematic-debugging, brainstorming, using-git-worktrees, subagent-driven-development).
Mandatory workflow – the agent checks for the appropriate skill before every action, ensuring the process is followed, not merely suggested.
Sub‑agent orchestration – each implementation task spawns a fresh sub‑agent that first validates spec compliance and then reviews code quality before committing.
Automated plan generation – designs are broken into 2‑5‑minute tasks with explicit file paths, code snippets and verification steps.
Built‑in TDD enforcement – failing tests are written first, code is deleted after the green phase, and the test-driven-development skill monitors the RED‑GREEN‑REFACTOR loop.
Git worktree isolation – a separate branch and workspace are created automatically, keeping the main line pristine until you merge.
Optional visual telemetry – a tiny logo reports only the Superpowers version (opt‑out via SUPERPOWERS_DISABLE_TELEMETRY).


How to get it

Superpowers is distributed as a plugin for dozens of AI coding environments. Install the appropriate package for your harness; the same skill set works everywhere.

# Claude Code (official marketplace)
/plugin install superpowers@claude-plugins-official

# Antigravity
agy plugin install https://github.com/obra/superpowers

# Codex CLI
/plugins   # then search “superpowers” and install

# Cursor
/add-plugin superpowers

# Devin CLI
devin plugins install obra/superpowers

# Factory Droid
droid plugin marketplace add https://github.com/obra/superpowers
droid plugin install superpowers@superpowers

# Gemini CLI
gemini extensions install https://github.com/obra/superpowers

# GitHub Copilot CLI
copilot plugin marketplace add obra/superpowers-marketplace
copilot plugin install superpowers@superpowers-marketplace

# Grok Build CLI
grok plugin install superpowers@xai-official --trust

# Kimi Code
/plugins install https://github.com/obra/superpowers

# OpenCode
# Follow the .opencode/INSTALL.md instructions

# Pi
pi install git:github.com/obra/superpowers

# Hermes Agent
hermes plugins install obra/superpowers --enable

After installation, the plugin’s using-superpowers bootstrap runs at session start (or after compaction for Pi), so the agent is instantly equipped with the full workflow.

The basic workflow
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brainstorming – the agent asks probing questions, iterates on ideas, and saves a chunked design doc.
using-git-worktrees – creates an isolated branch, sets up the project, and records a clean test baseline.
writing-plans – converts the design into bite‑size tasks with explicit paths and verification steps.
subagent-driven-development (or executing-plans) – each task is handed to a fresh sub‑agent, reviewed twice, then committed.
test-driven-development – enforces RED‑GREEN‑REFACTOR for every change.
requesting-code-review – inserts a human‑grade review between tasks, blocking progress on critical issues.
finishing-a-development-branch – runs final test checks, offers merge/PR options, and cleans up the worktree.


Technical highlights


Skills are pure JSON‑defined actions that can be invoked by any supported agent; no extra tooling is required.
Sub‑agents are sandboxed, ensuring that a failure in one task never contaminates the whole session.
The library includes meta‑skills (writing-skills, using-superpowers) so teams can extend the system while preserving compatibility.
All skill‑behaviour tests live in evals/ and use the superpowers-evals harness; plugin infrastructure is validated in tests/ via dedicated run scripts.
License is MIT, allowing unrestricted commercial use; enterprise customers can obtain paid support via sales@primeradiant.com.


Who should use it


Developers who rely on AI coding assistants (Claude, Cursor, Copilot, Gemini, etc.) and want the output to follow professional engineering practices.
Teams that need reproducible, test‑first code generation without manually orchestrating prompts.
Enterprises seeking a plug‑and‑play “AI‑pair‑programmer” that enforces TDD, code review and branch hygiene out of the box.
Open‑source contributors interested in extending a skill‑based framework for AI agents.


Community & support

Created by Jesse Vincent and Prime Radiant. Join the Discord (https://discord.gg/35wsABTejz) for help, share your projects, and watch for release notes at primeradiant.com/superpowers/.

Takeaway – Superpowers turns a chatty code bot into a disciplined engineer, delivering test‑first, review‑ready software without the hassle.

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🧠 Channel: https://t.me/GithubRe
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🌟 cursor/plugins caught my eye on GitHub Trending today.

🔗 https://github.com/cursor/plugins
📝 Cursor plugin specification and official plugins
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What is the cursor/plugins repository?
It is the official collection of Cursor plugins that extend the AI‑powered coding assistant with ready‑to‑use integrations for the most popular developer tools, frameworks, and SaaS products. Each plugin lives in its own folder and ships with a self‑contained .cursor-plugin/plugin.json manifest, so you can drop it into a Cursor workspace and start using it instantly.

Key features at a glance

Huge variety – from utility plugins like Teaching and Continual Learning, to dev‑ops helpers such as Cursor Team Kit and Thermos, all the way to productivity and SaaS connectors (Gmail, Google Drive, Salesforce, Zoom, etc.).
Marketplace‑ready – the root .cursor-plugin/marketplace.json indexes every plugin, enabling Cursor’s built‑in marketplace UI to discover, install, and update them automatically.
Modular architecture – each plugin contains:

plugin-name/
├── .cursor-plugin/
│   └── plugin.json        # plugin manifest
├── skills/                # SKILL.md files describing agent capabilities
├── rules/                 # .mdc rule files that shape the agent’s behaviour
├── mcp.json               # MCP server definitions (if needed)
├── README.md
├── CHANGELOG.md
└── LICENSE
    

Typed SDK support – the Cursor SDK plugin shows how to build custom scripts and automations with the TypeScript SDK.
Parallelism & orchestration – plugins like Thermos and Orchestrate let you fan out large tasks to multiple cloud agents, apply rubrics, and merge‑ready PR flows.
Open source & MIT licensed – you can fork, tweak, or contribute back without legal hurdles.


How to use a plugin
1. Open Cursor’s Marketplace panel.
2. Search for the desired name (e.g., gmail or pr-review-canvas).
3. Click Install; Cursor copies the plugin folder into your workspace and registers the manifest.
4. The plugin’s skills/ and rules/ are now available to the coding agent – you can invoke them with natural‑language prompts like “draft a reply to this email” or “run a security audit on the new branch”.
For developers who want to create their own plugin, the Create Plugin template provides a scaffold and a validation script that checks the manifest and required files before publishing.

Technical highlights

Manifest‑driven design – plugin.json declares capabilities, required permissions, and entry points, making the runtime discovery completely declarative.
Skill‑first approach – each plugin can ship one or more SKILL.md files with front‑matter that describes the agent’s “skill set”, enabling Cursor to surface contextual help and autocomplete suggestions.
Rule engine integration – .mdc rule files let you encode safety checks, style guides, or custom validation logic that the agent obeys automatically.
Multi‑agent orchestration – plugins such as Thermos and Orchestrate define planners, workers, and verifiers in mcp.json, allowing parallel execution and structured hand‑offs.
Third‑party connectors – OAuth handling, API wrappers, and UI components are baked into the SaaS plugins, so the agent can read/write Gmail, fetch Zoom transcripts, or update Salesforce records without extra code.


Who should explore this repo?
(1/2)

Cursor power users who want to supercharge their AI assistant with domain‑specific knowledge.
DevOps & SRE teams looking for automated code‑review, security audit, and CI pipelines.
Productivity hackers who wish to control email, calendar, and CRM directly from the editor.
Plugin developers interested in the manifest‑first pattern and the TypeScript SDK for building new integrations.


Quick start example (installing the Gmail plugin)

# Inside a Cursor workspace, open the Marketplace UI
# Search for "gmail" and click Install
# After installation, you can run:
cursor ask "Draft a reply to the last email from Alice about the Q3 budget."

Bottom line
The cursor/plugins repo is a plug‑and‑play ecosystem that lets you turn the Cursor AI into a full‑featured development assistant, a productivity hub, and a custom‑integration platform—all under an open‑source MIT license.

Turn a single AI into a whole team with just a click.

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🧠 Channel: https://t.me/GithubRe
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🎯 santifer/career-ops landed on trending. Worth a proper look.

🔗 https://github.com/santifer/career-ops
📝 Open-source AI job search: scan job portals, evaluate listings with a structured A-F rubric into a 1.0-5.0 score, tailor your CV, track applications — runs locally in your AI coding CLI (Claude Code, Codex, OpenCode, Antigravity…)
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Purpose
career‑ops is an open‑source, AI‑driven command‑center that turns any AI coding CLI into a personal job‑search assistant. Instead of juggling spreadsheets and endless job boards, the tool automatically scans listings, evaluates fit, customizes your résumé and cover letter, and keeps every interaction in a single, searchable pipeline.

How it works
You point the CLI at a job URL (or run a search query) and a fleet of “sub‑agents” spring into action. Using Playwright to navigate portals (Greenhouse, Lever, Ashby, company career pages, etc.), the agents compare the posting with the CV you feed them, reason about role relevance, and output a structured A‑H evaluation plus a legitimacy check (Block G). The system never clicks “apply” for you – it hands you polished PDFs, email drafts, and contact suggestions, letting you make the final call.

Key features
- Auto‑pipeline: one‑line career-ops evaluate gives a full score, PDF résumé, and tracker entry.
- A‑H Evaluation: role summary, CV match, level strategy, compensation research, personalization, interview prep (STAR + R), plus a scam‑detector block.
- ATS‑optimized PDFs: keyword‑injected CVs with a clean Space Grotesk / DM Sans design.
- Cover‑letter generator: research‑backed drafts with four angle prompts, interactive approval, and ready‑to‑print PDF.
- Application email drafts: recruiter, referral, or cold‑outreach templates that include subject lines, attachment checklists, and fit bullet points.
- Portal scanner: 100+ pre‑configured companies; custom queries across major ATS platforms.
- Funded‑company discovery: career-ops company:funded surfaces newly funded orgs with public‑feed diagnostics.
- Batch processing: evaluate ten‑plus listings in parallel using headless workers (claude -p or opencode run).
- Dashboard TUI: a terminal UI to browse, filter, and sort your pipeline without leaving the console.
- Human‑in‑the‑loop: AI recommends, you decide; the tool never auto‑submits.
- Interview suite: prep plans, mock sessions with feedback, red‑flag detector, and post‑interview debriefs.
- Offer stage helper: contract clause walkthrough, lawyer‑question list, and salary‑gap analyzer.
- Follow‑up engine: cadence calculator, seeded reminders, and reply classification that auto‑updates the tracker.
- Pattern analysis: rejection trends, funnel statistics, ghost‑job detection.
- Plugin system: optional integrations for Gmail, Notion, Apify, plus a community registry.
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Typical usage flow
1. Install the latest release (`npm i -g @santifer/career-ops`).
2. Seed your profile: career-ops init and feed your master CV, career story, and preferences.
3. Run a single evaluation: career-ops evaluate https://jobs.example.com/12345.
4. Review the A‑H score; if it’s ≥ 4.0/5, generate the tailored PDF with career-ops pdf or the cover letter with career-ops cover.
5. Use career-ops email to draft a recruiter message, or career-ops contacto to discover the hiring manager’s LinkedIn handle.
6. Keep everything organized in the TUI (`career-ops dashboard`) where you can filter by score, company, or stage.
7. When you land an interview, switch to career-ops interview for prep scripts and mock Q&A.
8. After an offer, run career-ops offer-prep and career-ops salary-gap.mjs to negotiate confidently.

Technical highlights
- Built with Claude Code and runs on any “agent‑skill‑standard” CLI (Claude, OpenCode, Antigravity, Codex, Qwen, Kimi, GitHub Copilot, Grok Build, etc.).
- Playwright powers reliable headless browsing across diverse ATS platforms.
- Core written in Node.js with optional Go helpers for high‑performance parallelism.
- Data integrity checks automatically merge duplicates, normalize statuses, and enforce a single source of truth.
- Extensible plugin architecture lets you connect to external services without altering the core.

Who should care
- Technical professionals (engineers, data scientists, AI researchers) who spend hours filtering noisy listings.
- Mid‑career talent looking for a systematic, evidence‑based way to target high‑impact roles.
- Solo founders or freelancers who need a reproducible hiring pipeline without hiring a recruiter.
- Developers who already use an AI coding CLI and want to extend its capabilities into career management.

Takeaway
career‑ops gives you the AI recruiter you wish you had, turning chaotic job hunting into a data‑driven, high‑precision operation.

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🔍 Deep-diving into akitaonrails/ai-memory — fresh off the trending list.

🔗 https://github.com/akitaonrails/ai-memory
📝 Solution for long term memory for agent coding CLIs and to facilitate handoff between different agent vendors
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Purpose
ai‑memory gives AI coding agents a durable, project‑local “brain”. When a session ends, the agent’s observations are turned into a concise markdown wiki that the next agent can read, letting you switch from Claude Code to OpenAI Codex—or any other supported harness—without re‑explaining the problem.

Key Features
- Zero‑friction lifecycle capture: hooks automatically collect sanitized prompts, tool usage, and session boundaries. Observations are limited to a few kilobytes but stored reliably in a git‑backed markdown repo.
- Managed workstreams: ai-memory run <agent> wraps the native agent command, creates a portable visible‑event ledger, and resumes the logical workstream across different harnesses (Claude, Codex, Command Code, Pi, etc.).
- Per‑repository capture exclusions: a simple [capture] ignore_paths marker file tells the system which files or tool events to drop before they reach the memory spool.
- Optional per‑operator memory slots: on shared servers, the [slots] per_user = true setting isolates context per user while keeping the wiki project‑wide.
- Cross‑agent handoffs: the next agent receives a “where you left off” block automatically, so you can quit one model and start another hours later in the same directory.
- Plain‑text wiki: the memory is ordinary markdown stored in a git repo—searchable with grep, viewable in Obsidian, and easy to back up with rsync. No vector DB, no manual write_note steps.

Typical Usage
1. Install the binary (Linux, macOS, Windows) or pull the Docker image.
2. Run the installer for your agent, e.g. install-mcp --client claude or install-hooks --agent codex.
3. Start a session normally, or launch through the wrapper:

ai-memory run claude   # starts Claude Code with capture enabled
ai-memory run codex --yolo   # switches to Codex, resuming the same workstream

4. When the session finishes, optionally run ai-memory finalize-session --agent <agent> to force a final summary.
5. The generated markdown lives under .ai-memory/wiki/; open it, edit, or let the next agent consume it automatically.

Technical Highlights
- Built in Rust (requires 1.95+), delivering a single static binary for all major platforms.
- Full Linux/amd64, Linux/arm64, macOS (x86_64 & aarch64), Windows (exe & Docker) support; native systemd units and AUR packages are provided for Linux.
- Integration matrix covers 20+ agents (Claude Code, Codex, Command Code, Pi, Kimi, Kiro, OpenCode, Gemini, etc.) via MCP configs and lifecycle hooks.
- Supports major LLM providers (Anthropic, OpenAI, Gemini, OpenCode) and embedding services (Voyage, Ollama, LM Studio).
- Optional “session‑aware” mode creates a local stdio bridge for per‑session isolation.
- All captured data is sanitized and size‑capped (16 KiB for prompts, 2 KiB for tool excerpts) before being written to the markdown ledger.
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Who Should Care
- Developers building or operating AI‑assisted coding tools who need continuity across model switches.
- Teams running multi‑agent pipelines (e.g., Claude for design, Codex for implementation) and wanting a single source of truth.
- Researchers studying long‑term LLM interaction, because the wiki provides a clean, searchable record of every observed turn.
- Ops engineers who prefer file‑based storage over managing a separate vector database.

Bottom line
ai‑memory turns fleeting LLM chats into a living, searchable project wiki, letting you pick up exactly where any AI left off—without the overhead of a database or manual note‑taking.

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🧠 Channel: https://t.me/GithubRe
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🔍 Deep-diving into agent-substrate/substrate — fresh off the trending list.

🔗 https://github.com/agent-substrate/substrate
📝 Agent Substrate: the core system
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Agent Substrate – a high‑density runtime for massive numbers of agents

What it is
Agent Substrate is a control‑plane system that lets you run thousands of “actor” processes (think AI agents, micro‑services, sandboxed tools) on a tiny pool of Kubernetes pods. By treating each actor as a lightweight sandbox that spends most of its time idle, Substrate can suspend, snapshot, and instantly resume them on any available worker, achieving sub‑second “teleport” between pods.

Key features

Sub‑second suspend/resume – actors are hibernated to RAM or disk and brought back in
State‑preserving snapshots – full memory + filesystem state is saved, so an actor picks up exactly where it left off.
Heavy multiplexing – >30× oversubscription (e.g., 250 stateful actors on 8 pods) by “juggling” actors across workers.
Sandbox‑agnostic – supports gVisor, microVMs, or any OCI container; works with any AI framework (LangChain, Claude Code, MCP, etc.).
Kubernetes‑native – builds on Pods, HPA, and autoscaling; the substrate control plane adds agent‑specific scheduling.
Request parking – router holds inbound requests when workers are saturated instead of returning 503.
Observability & security – built‑in logging, metrics, tracing, JWT auth, and a documented threat model.


How you use it
1. Install the required tools (go, kubectl, docker) – the scripts will pull kind or GKE resources for you.
2. Deploy the substrate system with the provided hack/install-ate-kind.sh (local) or hack/install-ate.sh (GKE) scripts.
3. Create an atespace (a namespace for actors) and launch an actor via the kubectl ate CLI.
4. Optionally forward the router service to your workstation and talk to the actor with a normal HTTP call.

# quick local start
hack/create-kind-cluster.sh
hack/install-ate-kind.sh --deploy-ate-system
hack/install-ate-kind.sh --deploy-demo-counter
go install ./cmd/kubectl-ate

kubectl ate create atespace demo
kubectl ate create actor my-counter-1 -a demo --template=ate-demo-counter/counter
kubectl port-forward -n ate-system svc/atenet-router 8000:80

Then in another terminal:

curl -X POST -H "Host: my-counter-1.demo.actors.resources.substrate.ate.dev" -i http://localhost:8000/

Technical highlights

Control‑plane API – cmd/ateapi exposes gRPC endpoints for actor/worker lifecycle.
Node supervisor – cmd/atelet runs as a DaemonSet, handling snapshot creation, state transfer, and worker health.
WorkerPool & ActorTemplate CRDs – declarative resources let you describe how many workers to keep, what sandbox to use, and which binaries to run.
Persistent state – snapshots are stored in a distributed KV store (Valkey) and can be restored on any worker.
Framework‑agnostic – because Substrate deals only with OCI containers, you can drop in LangChain agents, custom ADK actors, or MCP servers without code changes.
Autoscaling integration – WorkerPools can be scaled by a Horizontal Pod Autoscaler fed by Prometheus metrics.


Who should care
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AI platform engineers building large fleets of stateful agents (e.g., multi‑turn chat bots, tool‑calling assistants).
DevOps teams that need to run many short‑lived, sandboxed workloads on limited hardware.
Research groups experimenting with reinforcement‑learning loops that involve many concurrent agents, inference, and training steps.
Security‑focused developers who want isolated execution (gVisor/microVM) with fast start‑up times.


Current status – early‑development, not production‑ready; APIs may change. The project targets the latest stable Kubernetes release and the previous minor version.

Community & help – join the ate‑dev Google Group, attend the weekly Thursday call, or hop into the CNCF Slack channels #substrate-users and #substrate-dev.

Takeaway – Agent Substrate lets you pack hundreds of stateful agents onto a handful of pods, turning “many tiny processes” into a single, fast, and manageable cloud service.

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🧠 Channel: https://t.me/GithubRe
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