Morning Singularity Digest - 2026-06-07

Context

The best-benchmarked open-source AI memory system.

What's new

The best-benchmarked open-source AI memory system.

Key details

• Verbatim storage, pluggable backend, 96.6% R@5 raw on LongMemEval — zero API calls.
• MemPalace has no other official websites.
• The only official sources are this GitHub repository, the PyPI package, and the docs at mempalaceofficial.com.
• Any other domain (including .tech , .net , or other .com variants) is an impostor and may distribute malware.

Results & evidence

• Verbatim storage, pluggable backend, 96.6% R@5 raw on LongMemEval — zero API calls.
• Important Claude Code sessions expire in 30 days without auto-save hooks wired.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

| Topic | What You'll Learn | |---|---| | Token Optimization | Model selection, system prompt slimming, background processes | | Memory Persistence | Hooks that save/load context across sessions automatically | | Continuous Learning | Auto-extract patterns...

What's new

Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.

Key details

• Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
• Language: English | Português (Brasil) | 简体中文 | 繁體中文 | 日本語 | 한국어 | Türkçe | Русский | Tiếng Việt | ไทย | Deutsch | Español 182K+ stars | 28K+ forks | 170+ contributors | 12+ language ecosystems | Cross-harness agent workflows Language / 语言 / 語言 / Dil / Язык...
• Built from real-world multi-harness engineering workflows.
• A complete system: skills, instincts, memory optimization, continuous learning, security scanning, and research-first development.

Results & evidence

• Language: English | Português (Brasil) | 简体中文 | 繁體中文 | 日本語 | 한국어 | Türkçe | Русский | Tiếng Việt | ไทย | Deutsch | Español 182K+ stars | 28K+ forks | 170+ contributors | 12+ language ecosystems | Cross-harness agent workflows Language / 语言 / 語言 / Dil / Язык...
• Production-ready agents, skills, hooks, rules, MCP configurations, and legacy command shims evolved over 10+ months of intensive daily use building real products.
• ECC v2.0.0-rc.1 adds the public Hermes operator story on top of that reusable layer: start with the Hermes setup guide, then review the rc.1 release notes and cross-harness architecture.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Give an AI agent read/write/deepen access to your real Obsidian vault as a living knowledge graph.

What's new

That works fine until you want your agent to actually understand something — to fold a new observation into existing knowledge, connect it to related ideas, and let the graph grow from experience.

Key details

• Most AI memory systems are databases.
• They store what happened and retrieve it when relevant.
• That works fine until you want your agent to actually understand something — to fold a new observation into existing knowledge, connect it to related ideas, and let the graph grow from experience.
• This library bridges your AI agent to a real Obsidian vault through the Local REST API plugin.

Results & evidence

• npm install obsidian-agent-bridge Prerequisites: - Obsidian installed and running - Local REST API plugin enabled (runs on port 27124) - Your API key from the plugin settings const { ObsidianGraph } = require('obsidian-agent-bridge'); const graph = new Obsi...

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Project the aircraft passing overhead onto your ceiling, in real time — an X-ray through the roof.

What's new

Project the aircraft passing overhead onto your ceiling, in real time — an X-ray through the roof.

Key details

• 🛰️ Get notified when I launch on a crowdfunding platform → skylightceiling.com A ready-made kit is coming.
• Join the waitlist for early access & launch pricing.
• ceiling-main.mp4 Skylight decodes ADS-B from a cheap RTL-SDR radio and renders the planes physically flying over you onto a ceiling-pointed projector.
• A jet you'd hear overhead glides across your ceiling at the same moment — labeled with its airline, type, and where it's headed.

Results & evidence

• - Smooth motion — interpolates the ~1 Hz fixes to 60 fps by rendering slightly in the past and tweening between real positions (no teleporting).

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

OpenAI shares guidance on third-party AI evaluations, covering how to assess model capabilities, safeguards, and validity for frontier systems.

What's new

OpenAI shares guidance on third-party AI evaluations, covering how to assess model capabilities, safeguards, and validity for frontier systems.

Key details

• OpenAI shares guidance on third-party AI evaluations, covering how to assess model capabilities, safeguards, and validity for frontier systems.

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

| Topic | What You'll Learn | |---|---| | Token Optimization | Model selection, system prompt slimming, background processes | | Memory Persistence | Hooks that save/load context across sessions automatically | | Continuous Learning | Auto-extract patterns...

What's new

Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.

Key details

• Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
• Language: English | Português (Brasil) | 简体中文 | 繁體中文 | 日本語 | 한국어 | Türkçe | Русский | Tiếng Việt | ไทย | Deutsch | Español 182K+ stars | 28K+ forks | 170+ contributors | 12+ language ecosystems | Cross-harness agent workflows Language / 语言 / 語言 / Dil / Язык...
• Built from real-world multi-harness engineering workflows.
• A complete system: skills, instincts, memory optimization, continuous learning, security scanning, and research-first development.

Results & evidence

• Language: English | Português (Brasil) | 简体中文 | 繁體中文 | 日本語 | 한국어 | Türkçe | Русский | Tiếng Việt | ไทย | Deutsch | Español 182K+ stars | 28K+ forks | 170+ contributors | 12+ language ecosystems | Cross-harness agent workflows Language / 语言 / 語言 / Dil / Язык...
• Production-ready agents, skills, hooks, rules, MCP configurations, and legacy command shims evolved over 10+ months of intensive daily use building real products.
• ECC v2.0.0-rc.1 adds the public Hermes operator story on top of that reusable layer: start with the Hermes setup guide, then review the rc.1 release notes and cross-harness architecture.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Give an AI agent read/write/deepen access to your real Obsidian vault as a living knowledge graph.

What's new

That works fine until you want your agent to actually understand something — to fold a new observation into existing knowledge, connect it to related ideas, and let the graph grow from experience.

Key details

• Most AI memory systems are databases.
• They store what happened and retrieve it when relevant.
• That works fine until you want your agent to actually understand something — to fold a new observation into existing knowledge, connect it to related ideas, and let the graph grow from experience.
• This library bridges your AI agent to a real Obsidian vault through the Local REST API plugin.

Results & evidence

• npm install obsidian-agent-bridge Prerequisites: - Obsidian installed and running - Local REST API plugin enabled (runs on port 27124) - Your API key from the plugin settings const { ObsidianGraph } = require('obsidian-agent-bridge'); const graph = new Obsi...

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

The open-source app everyone uses to manage agents at work Quickstart · Docs · GitHub · Discord · Twitter · Website full-tour.webm Open-source orchestration for teams of AI agents.

What's new

The open-source app everyone uses to manage agents at work Quickstart · Docs · GitHub · Discord · Twitter · Website full-tour.webm Open-source orchestration for teams of AI agents.

Key details

• If OpenClaw is an employee, Paperclip is the company.
• Paperclip is a Node.js server and React UI that orchestrates a team of AI agents to run a business.
• Bring your own agents, assign goals, and track work and costs from one dashboard.
• Under the hood: org charts, budgets, governance, goal alignment, and agent coordination.

Results & evidence

• | Step | Example | | |---|---|---| | 01 | Define the goal | "Build the #1 AI note-taking app to $1M MRR." | | 02 | Hire the team | CEO, CTO, engineers, designers, marketers — any bot, any provider.
• | | 03 | Approve and run | Review strategy.
• | - ✅ You want to build autonomous AI companies - ✅ You coordinate many different agents (OpenClaw, Codex, Claude, Cursor) toward a common goal - ✅ You have 20 simultaneous Claude Code terminals open and lose track of what everyone is doing - ✅ You want age...

Limitations / unknowns

• When they hit the limit, they stop.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

A collection of DESIGN.md files analysis by popular brand design systems.

What's new

DESIGN.md is a new concept introduced by Google Stitch.

Key details

• Drop one into your project and let coding agents generate a matching UI.
• Copy a DESIGN.md into your project, tell your AI agent “build me a page that looks like this,” and generate high-quality UI that stays visually consistent with the design language.
• Built with real design depth — including analyzed patterns, tokens, and rules — for high-quality UI generation, not surface-level outputs.
• DESIGN.md is a new concept introduced by Google Stitch.

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Язык: Русский | English Поисковый CLI для LLM-агентов.

What's new

asearch ищет одновременно в вебе, Hacker News, Reddit, GitHub, YouTube, X/Twitter и коде, а также через Tavily, Exa, Brave и ещё 6 API.

Key details

• asearch ищет одновременно в вебе, Hacker News, Reddit, GitHub, YouTube, X/Twitter и коде, а также через Tavily, Exa, Brave и ещё 6 API.
• Не засоряет контекст агента: сначала возвращает компактные метаданные, потом агент читает только нужные страницы через пагинацию.
• Один Go-бинарь, единственная зависимость — Cobra.
• npm i -g agent-asearch # Zero-config — работает сразу, ничего не нужно asearch open --query "claude code plugins" --source hn,reddit # Web поиск — DDG + Wikipedia + Bing, тоже без ключей asearch open --query "claude code plugins" --source web # Поиск по код...

Results & evidence

• asearch ищет одновременно в вебе, Hacker News, Reddit, GitHub, YouTube, X/Twitter и коде, а также через Tavily, Exa, Brave и ещё 6 API.
• — 35B-страниц | | serper | 🔑 | Google SERP (2500 бесплатно/мес) | | serpapi | 🔑 | 40+ поисковиков | | perplexity | 🔑 | AI-ответы с цитатами | | you | 🔑 | You.com поиск | | firecrawl | 🔑 | JS-рендеринг страниц | | parallel | 🔑 | Parallel.ai поиск | | ✅ встро...
• Сначала смотрите метаданные, потом читайте нужные страницы: asearch open --query "rust async benchmarks" --source web,hn # → {"ok":true,"sid":"...","total":42,...} asearch results read -s SID --seq 1 --limit 10 asearch results read -s SID --seq 11 --limit 1...

Limitations / unknowns

• Сначала смотрите метаданные, потом читайте нужные страницы: asearch open --query "rust async benchmarks" --source web,hn # → {"ok":true,"sid":"...","total":42,...} asearch results read -s SID --seq 1 --limit 10 asearch results read -s SID --seq 11 --limit 1...
• Читай результаты маленькими страницами: asearch results read -s SID --seq 1 --limit 20 Фильтруй по источнику перед чтением: asearch results filter -s SID --source reddit Для пайпов используй --raw: asearch results read -s SID --raw | head -50 Всегда закрыва...
• Сохрани sid, читай через `asearch results read -s SID --seq 1 --limit 20`, фильтруй через `asearch results filter -s SID --source reddit`.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Hello HN , Cordium is a FOSS, self-hosted, general-purpose sandbox platform that I've been working on for a long time now that is built on Kubernetes and Octelium https:&...

What's new

I also want to clarify that Cordium, while opensourced a few days ago, is not a new project, the development of the project dates back to 2022 (see the older in https:/...

Key details

• Cordium can be used for various persistent/ephemeral long/short-lived workloads, including coding for developers with VSCode, Zed, etc.
• self-hosted GitHub Codespaces alternative), AI agent tasks (i.e.
• FOSS alternative to AI sandbox products such as E2B, Daytona, etc.), CI/CD workloads (e.g.
• building and publishing Docker images etc.), and more importantly for secretless remote access to infrastructure for devs and automated workloads from within the sandboxes.

  The key differentiator here for Cordium, in comparison with other dev environments...

Results & evidence

• I also want to clarify that Cordium, while opensourced a few days ago, is not a new project, the development of the project dates back to 2022 (see the older in https:/...
• In other words, this is not a toy project and it's meant to be used in production even though it's not quite ready to be labeled v1.0 yet.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler

What's new

Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler

Key details

• Profiling in PyTorch (Part 1): A Beginner's Guide to torch.profiler

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Sponsors especially OPENAI CODEX voucher usage for codex - openAI challange

What's new

Sponsors especially OPENAI CODEX voucher usage for codex - openAI challange

Key details

• Sponsors especially OPENAI CODEX voucher usage for codex - openAI challange

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.

Context

Granite Embedding Multilingual R2: Open Apache 2.0 Multilingual Embeddings with 32K Context — Best Sub-100M Retrieval Quality

What's new

Granite Embedding Multilingual R2: Open Apache 2.0 Multilingual Embeddings with 32K Context — Best Sub-100M Retrieval Quality

Key details

• Granite Embedding Multilingual R2: Open Apache 2.0 Multilingual Embeddings with 32K Context — Best Sub-100M Retrieval Quality

Results & evidence

• No hard numbers surfaced in the source text; treat claims as directional until benchmarks appear.

Limitations / unknowns

• Generalization outside curated tasks is still unclear.

Next-step validation checks

• Reproduce one claim with a public baseline and fixed evaluation settings.
• Check robustness on out-of-distribution or long-context cases.
• Track whether independent teams report matching results.