🎯 PostHog/posthog landed on trending. Worth a proper look.

🔗 https://github.com/PostHog/posthog
📝 🦔 PostHog is the leading platform for building self-driving products. Our developer tools – AI observability, analytics, session replay, flags, experiments, error tracking, logs, and more – capture all the context agents need to diagnose problems, uncover opportunities, and ship fixes. Steer it all from Slack, web, desktop, or the MCP.
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What is PostHog?
PostHog is an open‑source platform that gives product teams every tool they need to turn raw usage data into “self‑driving” products. It captures events, errors, sessions and more, then lets you analyze, experiment, and even automate fixes—all from a single dashboard you can run in the cloud or on your own servers.

Key Features at a Glance
- Self‑driving mode: Auto‑generated reports and PRs that turn signals (rage clicks, failed queries, etc.) into actionable code.
- Product & web analytics: Autocapture events or instrument manually, explore with visual dashboards or raw SQL.
- Session replays: Watch real user sessions to spot friction instantly.
- Feature flags & experiments: Roll out changes to targeted cohorts, run A/B tests, and measure impact without writing code.
- Error tracking & logs: Capture exceptions, set alerts, and search logs alongside product data.
- Surveys & data warehouse sync: Collect feedback and sync with tools like Stripe or HubSpot; query everything together.
- AI observability & workflows: Record LLM traces, latency and cost; automate actions or messages via visual workflows.
- Multi‑channel control: Manage everything from Slack, the web UI, PostHog Desktop, or the MCP (Machine‑Code‑Protocol) that plugs into editors such as Claude Code or Cursor.

Getting Started – Two Easy Paths

1. PostHog Cloud (recommended) – Sign up for a free cloud account (US or EU). You get 1 M events, 5 k recordings, 1 M flag checks, 100 k exceptions and 1 500 survey responses each month at no cost. After the free tier you pay only for what you use.

2. Self‑hosting (hobby deploy) – If you prefer full control, launch a Docker‑based hobby instance in one line (≈4 GB RAM needed):

/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/posthog/posthog/HEAD/bin/deploy-hobby)"

This open‑source deployment comfortably handles ~100 k events/month; beyond that the docs recommend migrating to PostHog Cloud.

Installing the SDK
After your instance is up, add the JavaScript snippet or any of the many SDKs (React, Next.js, Vue, Python, Node, Ruby, Go, .NET, Android, iOS, Flutter, etc.) to start sending data.

// Example: JavaScript snippet
posthog.init('YOUR_API_KEY', { api_host: 'https://app.posthog.com' })
posthog.capture('event_name', { property: 'value' })

You can also use the REST API directly or connect the MCP to bring PostHog into any code‑assistant workflow.
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Technical Highlights
- Monorepo architecture: All front‑end, back‑end and worker services live in a single repo, making cross‑feature changes straightforward.
- Docker first: Official Docker image (posthog/posthog) is the recommended deployment method; pulls are tracked by a badge on the README.
- MIT + EE licensing: Core is MIT‑licensed; the ee directory holds proprietary extensions under a separate license. A fully FOSS fork (posthog-foss) removes those parts.
- Scalable pipelines: Built‑in CDP lets you filter, transform, and route events to 25+ downstream tools or custom webhooks, either in real‑time or batch.
- SQL‑ready: All events land in ClickHouse, giving you fast, ad‑hoc SQL queries out of the box.

Who Should Use It?
- Product managers who need fast insight into activation, retention and revenue.
- Engineers looking to embed analytics, feature flags and error tracking without third‑party contracts.
- Data teams that want a unified warehouse for product‑level data alongside logs and external sources.
- Start‑ups and growth‑stage companies that value an open‑source core but also want a hassle‑free managed option.

Community & Contributions
PostHog welcomes PRs, feature votes, and bug reports. The repo includes a detailed docs/internal/monorepo-layout.md and product overviews. The company’s handbook, roadmap and hiring page are all public, reflecting a transparent, community‑first culture.

Punchy Takeaway
Turn raw product data into automatic fixes and smarter decisions – all without leaving your own stack.

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🧠 Channel: https://t.me/GithubRe
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Why it matters?
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🔍 Deep-diving into mahlernim/google-timeline-visualizer — fresh off the trending list.

🔗 https://github.com/mahlernim/google-timeline-visualizer
📝 Visualize your year in travel using your Google Location History (Timeline) data
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What it does
Turn a Google Maps Timeline export (the Timeline.json file) into a smooth, animated travel video that you can watch, share, or keep as an MP4. The app works completely offline – your location history never leaves the device.

Key features
- Choose exact dates or a month range, then preview the journey on a map.
- Pick camera styles: steady (fixed zoom) or dynamic (follows local movement).
- Set video length from 10 s up to 5 min; the UI warns when long renders will need extra storage.
- Export in square (480 p / 720 p / 1080 p) or portrait/landscape HD presets.
- Built‑in “My videos” library with thumbnails, titles, and quick share actions.
- Supports English, Korean, Japanese, Chinese (Simplified & Traditional), Spanish, French, German, Portuguese (BR).
- No Google sign‑in, no location permissions, no analytics – only the JSON you feed it and the map tiles from CARTO/OpenStreetMap.

How to use it

iPhone (web app)
1. Export Timeline.json from Google Maps (Settings → Personal content → Export Timeline data).
2. Open the web app here in Safari (iOS 16.4+).
3. Tap **Choose Timeline.json**, set the date range, pick a camera mode, accept the privacy notice.
4. Hit **Preview** → **Create MP4**. Keep the tab open until the render finishes; then download or share the video.

Android (stand‑alone app)
1. Download the latest APK from the repository’s release page.
2. Install (enable “install unknown apps” for the source if needed).
3. Open the app, tap **Create video**, load your Timeline.json, configure dates, camera, duration, then **Create video**.
4. The finished MP4 lands in Movies/Timeline Visualizer (Android 10+ auto‑saves).

Technical highlights
- Rendering pipeline: map tiles are pre‑fetched, GPS outlier filtering (conservative by default) cleans noisy points, long‑flight legs are interpolated on great‑circle arcs for smooth motion.
- Privacy‑first design: the only network call is to CARTO for map tiles; the Timeline JSON never leaves the device.
- Supported export formats: current Android/iOS direct‑array JSON, legacy “semanticSegments” JSON, raw location fallback with optional noise reduction, handling of E7, geo: and latLng strings, and routes crossing the International Date Line.
- Build & test: Android Studio with JDK 17, SDK 36, Build‑Tools 36.0.0. Run

./gradlew test lint assembleGithubDebug assemblePlayDebug

to verify.
- Desktop fallback: a Python 3.9+ script (visualizer.py) plus FFmpeg reproduces the same MP4 generation on any PC. Example command:

python -m pip install -r requirements.txt
python visualizer.py --input Timeline.json --year 2025 --camera-movement steady \
  --long-trip-compression balanced --output my_trip_2025.mp4

Who should care
- Travelers who want a quick visual recap of a trip without fiddling with video editors.
- Developers looking for a clean, offline‑only example of map‑based animation on Android.
- Privacy‑conscious users who refuse cloud services but still want to share a travel story.
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Takeaway
Turn raw location history into a polished travel movie in seconds—completely offline and with zero data leakage.

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

🔗 https://github.com/volcengine/OpenViking
📝 Self-evolving Context Database for AI Agents. Unify Agent Memory, Knowledge RAG and Skills.
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What is OpenViking?
OpenViking is an open‑source “context database” that lets AI agents treat their memories, resources, and skills as a regular virtual filesystem accessed through the viking:// protocol. Instead of sending opaque vectors to a black‑box store, an agent can ls, tree and find its own context just like a developer works with files.

Why it matters

One filesystem for everything – memories, project docs, code snippets and reusable skills each appear as folders or files under a single URI scheme.
Tiered loading (L0/L1/L2) – on write each entry is split into a short abstract (L0), a richer overview (L1) and the full detail (L2). The system only loads the layer deep enough for the current task, slashing token usage.
Recursive, context‑aware retrieval – a vector search first picks the highest‑scoring directory, then drills down layer by layer, delivering results together with their surrounding context.
Observable queries – every retrieval keeps a “browsing trajectory” you can inspect, making debugging wrong answers straightforward.
Sessions become memory – after a session ends, OpenViking extracts preferences and experience into long‑term memory automatically.


Key technical highlights

Virtual viking:// URI hierarchy (e.g. viking://resources/…, viking://user/{id}/memories/…).
Three‑tier content layers:

.abstract   # L0 ~100 tokens
.overview   # L1 ~2k tokens
(full file) # L2 full content, read on demand

Directory‑first vector search that preserves surrounding files, enabling “semantic‑aware” navigation.
Built‑in observability: every query logs the exact path it followed.
Plug‑and‑play integrations with major agents (Claude Code, OpenClaw, Hermes, LangChain, etc.) and support for multiple LLM providers (Volcengine, OpenAI, Ollama, Kimi, GLM).
CLI ov for managing resources, browsing, and searching without writing code.
Docker image and production‑grade HTTP service for scaling.


Getting started in minutes

pip install openviking --upgrade
openviking-server init      # interactive wizard creates ~/.openviking/ov.conf
openviking-server doctor    # sanity‑check the config
openviking-server           # launch the background service

With the server running, the CLI lets you explore:

ov status
ov add-resource https://github.com/volcengine/OpenViking   # optional --wait
ov ls viking://resources/
ov tree viking://resources/volcengine -L 2
ov find "what is openviking"
ov grep "openviking" --uri viking://resources/volcengine/OpenViking/docs/en

If you just want to play, the web‑hosted OpenViking Studio offers a live demo with semantic search and a multi‑agent hub – no installation required.

Who should use it?

AI developers building agents that need deterministic, inspectable memory.
Research teams experimenting with context‑engineering and long‑term memory.
Product teams that want to give LLMs access to internal docs, codebases, and user preferences without leaking raw files.
DevOps engineers looking for a self‑hosted, AGPL‑licensed alternative to proprietary vector stores.
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Integration highlights
OpenViking provides ready‑made adapters for dozens of agents (Claude Code, Codex, OpenClaw, Hermes, Cursor, TRAE, LangChain, etc.). Each integration automatically injects recall calls and commits session memory, so you can upgrade an existing bot to “filesystem‑aware” context with a few configuration steps.

Desktop helper (beta) – a GUI console for macOS and Windows that visualises session traces, manages local memory/skills, and syncs them to the server.

VikingBot – an opinionated AI‑agent framework built on top of OpenViking. Install with pip install "openviking[bot]", start the server with --with-bot, and chat via ov chat.

Production deployment – run the server as a standalone HTTP service, use the official Docker image, or follow the detailed deployment guide for scaling, monitoring, and secure configuration.

Commercial edition note – the repository ships a fully functional, un‑restricted AGPL‑v3 edition. The commercial “Managed SaaS” offering is a hosted service, not a feature‑locked version.

Takeaway: OpenViking turns AI memory into a searchable, tiered filesystem, giving agents the same transparent, file‑system tools developers already love.

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🧠 Channel: https://t.me/GithubRe
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🚀 Meet JuliusBrussee/caveman: a gem from today's GitHub trending list.

🔗 https://github.com/JuliusBrussee/caveman
📝 🪨 why use many token when few token do trick — Claude Code skill that cuts 65% of tokens by talking like caveman
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Caveman is a tiny‑but‑mighty toolkit that teaches your AI agents to speak “caveman” – saying less while still saying the right thing. It trims the flood of tokens that LLM‑backed assistants pour into every request, cutting costs without sacrificing correctness.

Why it matters
Large‑language‑model providers charge per token, so every extra word, log line, or JSON payload inflates your bill. Caveman’s two‑pronged approach saves token usage on both sides:

* Proxy mode – a local proxy that compresses what the model **reads** before it hits the provider.
* Skill mode – a plug‑in that makes the model’s **answers** concise while keeping code, files, and errors byte‑exact.

In a pinned Claude Code benchmark the proxy achieved 33.2 % fewer provider‑reported input tokens while passing all 18 exact‑answer checks.

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Key features

1. Caveman Proxy – One‑line wrapper around any supported agent (Claude, Gemini, Codex, …).
  caveman claude or caveman wrap --pixel claude

* Detects the payload type (JSON, log, code, diff, search result, plain text).
* Applies a content‑aware compressor that keeps the bits the model actually needs.
* Stores the original bytes in a content‑addressed “CCR” store; the model can retrieve them with caveman_retrieve.
* Three safe modes:
 • default – compresses when the transformation is smaller.
 • --off – only measures, leaves data untouched.
 • --pixel – renders dense text blocks to PNG so vision models see far fewer tokens.

2. Caveman Skill – Add a single skill to >30 agents so their replies are terse.
  npx skills add JuliusBrussee/caveman

* The model still receives full‑size tool results, files, and logs; only its spoken output is shortened.
* Works with agents ranging from Claude Code to open‑source helpers like aider.

3. Learn & Implement – A local analyzer that scans months of your agent history, scores token “sinks”, and suggests one‑line fixes.
  caveman learn – generates a “Cave Score” and a ranked list of wasteful patterns.
  caveman learn implement – launches your own agent to propose diff‑style edits, applies only with your approval, and re‑measures the impact.

4. Pixel Mode – Turns massive text slabs into grayscale PNG pages, letting vision models ingest the same information for a fraction of the token price.
  caveman wrap --pixel claude

* Example: 8 622 characters become a 1568×232 PNG, estimated to drop from ~2 597 text tokens to ~534 image tokens.

5. TOON encoder/decoder – A custom JSON re‑encoder that shrinks repetitive structures when it proves beneficial.
  caveman toon encode / caveman toon decode

6. Durable Memory – Store and recall raw bytes via caveman mem remember and caveman mem recall, enabling “off‑load” of frequently reused context.

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How to get started

Install the CLI (global)

npm install -g @caveman-ai/cli && caveman setup --install
caveman claude        # choose your provider (claude, codex, gemini, …)

Add the skill to an existing agent

npx skills add JuliusBrussee/caveman

Full installer (covers 30+ agents, Windows, macOS, Linux)
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curl -fsSL https://raw.githubusercontent.com/JuliusBrussee/caveman/v2.2.0/install.sh | bash

For a single agent you can also run the appropriate plugin command (Claude Code, Gemini CLI, etc.) – see the README for the exact syntax.

Once installed, run caveman learn to see where your tokens leak, then let the tool guide you through safe, consent‑gated fixes.

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Technical highlights

* Per‑type compressors – JSON (70‑90 % reduction), logs (85‑95 %), code (40‑70 %), diffs (60‑80 %), search results (80‑95 %), generic text/HTML (50‑80 %).
* Tree‑sitter integration (Go, Python, JS/TS) for structural code compression; pure‑Go fallback for Go‑only environments.
* BM25‑based relevance ranking combined with recency and error signals to pack the most useful context into the model’s token budget while preserving chronological order.
* Content‑addressed “CCR” store guarantees byte‑exact recovery; every transformation is logged with a clear “decline reason” when it would enlarge the payload.
* The proxy is a BSL‑1.1 runtime with an MIT‑licensed CLI – you can compile it yourself from source (Go + pnpm) if you prefer not to use the signed binaries.
* Benchmarks (Claude Code, 54 runs) show a 33.2 % reduction in input tokens and unchanged answer correctness; pixel mode on a 63.7 k‑char JSON+log slab drops estimated tokens from ~55 k to ~11 k.

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Who should care?

* Prompt engineers looking to squeeze more mileage out of token‑priced models.
* Dev teams that run continuous LLM‑assisted coding, CI, or debugging pipelines and see their bills balloon.
* Open‑source AI hobbyists using Claude, Gemini, Codex, or any of the 30+ supported agents.
* Anyone who wants to keep the “brain” of their AI big while making the “mouth” tiny.

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Takeaway – Talk less, think more: Caveman lets your agents stay smart without swallowing the token bill.

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🧠 Channel: https://t.me/GithubRe
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📌 Spotted on GitHub Trending: makeplane/plane — let's break it down.

🔗 https://github.com/makeplane/plane
📝 🔥🔥🔥 Open-source Jira, Linear, Monday, and ClickUp alternative. Plane is a modern project management platform to manage tasks, sprints, docs, and triage.
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Plane – Modern, open‑source project management

What’s it for?
Plane is a self‑hostable (or cloud‑hosted) tool that lets teams track issues, run cycles (formerly sprints), organise roadmaps and keep all the project data in one tidy place – without the endless admin overhead that comes with many “all‑in‑one” solutions.

Key features at a glance

Work Items – Rich‑text task editor, file uploads, sub‑properties and cross‑issue linking.
Cycles – Visual burn‑down charts and progress tracking to keep momentum.
Modules – Break huge projects into bite‑size, manageable chunks.
Views – Build custom filters, save them, and share with teammates.
Pages – AI‑enhanced note‑taking with a full‑featured editor; turn notes into actionable items in a click.
Analytics – Real‑time dashboards that surface trends, blockers and health metrics.


How to get started

1. Plane Cloud (quick‑start) – Sign up at app.plane.so and you’re ready to go, no servers to manage.

2. Self‑hosted – Pick the deployment style that matches your ops stack.

# Docker‑Compose (most common)
git clone https://github.com/makeplane/plane.git
cd plane
docker compose up -d

For Kubernetes, follow the official guide (link in the repo). Managed‑hosting options like Zenith are also listed.

Instance administration – Once up, admins can dive into “God mode” to tweak instance‑wide settings, data retention policies, SSO, etc.

Technical highlights


Frontend – Built with React Router, delivering a fast, SPA‑like experience.
Backend – Powered by Django, handling complex relational data and permissions.
Node.js – Supports the build pipeline and real‑time features.
Rich‑text editing, file storage, and AI integrations are all open‑source, making extensions straightforward.


Who should care?


Product managers and scrum masters who need a lightweight yet powerful issue tracker.
Engineering teams that want full control over data (self‑host) or a hassle‑free SaaS option.
Open‑source enthusiasts looking to contribute to a modern Django + React stack.
Start‑ups and small businesses that can’t afford pricey PM tools but still want analytics and custom views.


Community & contribution – Plane thrives on feedback via its forum, GitHub Discussions, and the issue tracker. Documentation is split into product (how‑to) and developer (API, self‑hosting) sections. Security reports go to security@plane.so. The project is GPL‑3.0‑AGPL licensed, encouraging both use and contribution.

Takeaway – Plane gives you the power of a full‑featured PM suite without locking you into a vendor, letting you focus on building, not on tool‑maintenance.

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🧠 Channel: https://t.me/GithubRe