Telepath's Television project describes a GUI layer that gives people and their personal agents a shared visual space for creating and working with persistent, malleable, interactive artifacts. It connects a lightweight server on the agent's machine, a skills bundle that teaches the agent how to create and modify artifacts, and a client app, either a macOS Electron app or a browser-based interface. The page emphasizes that Television works with any agent harness and model, and that it is currently a newly public, MIT-licensed repository with a small contributor base.
- Agents are recommended from specific harnesses such as Hermes, OpenClaw, Pi, Claude Code, or Codex.
- macOS is the primary supported path via an Electron app; Linux or macOS is required for the agent side, and Windows agents are not supported.
- The repo uses a spec-driven development process that is not yet open to outside contributors.
- It is built mostly in TypeScript, with JavaScript, CSS, HTML, and a little Python and Shell.
- Agents are recommended from specific harnesses such as Hermes, OpenClaw, Pi, Claude Code, or Codex.
- macOS is the primary supported path via an Electron app; Linux or macOS is required for the agent side, and Windows agents are not supported.
- The repo uses a spec-driven development process that is not yet open to outside contributors.
- It is built mostly in TypeScript, with JavaScript, CSS, HTML, and a little Python and Shell.
Chat On Steroids is an open-source desktop workspace that connects your local files and terminal to an existing ChatGPT conversation, allowing the model to read, edit, run tests, and manage terminals within your actual project rather than a sandbox. It operates through MCP and a companion component that observes and automates the ChatGPT browser UI, while offering worker-based task management with persistent context to handle multi-step jobs. The project is explicitly an independent beta that rides on your existing ChatGPT plan, meaning shared usage limits apply, and it ships for Windows x64, macOS Apple silicon, and Linux x64.
- Workers maintain their context across tasks so you don't re-explain project state on every handoff.
- Goal, Loop, Compact & Resume, and mid-run corrections address the all-or-nothing nature of long agentic jobs.
- The README is direct about not bypassing usage limits, account restrictions, or safety controls.
- Workers maintain their context across tasks so you don't re-explain project state on every handoff.
- Goal, Loop, Compact & Resume, and mid-run corrections address the all-or-nothing nature of long agentic jobs.
- The README is direct about not bypassing usage limits, account restrictions, or safety controls.
Yohei Nakajima writes about glance, a tool designed to allow users to ask an open vision-language model (VLM) typed questions about images and receive probability data directly on their own machine. Rather than generating new text or training models, it acts as a measurement harness that reads logits from frozen models—such as Qwen3-VL-4B by default—to provide yes/no answers, single-choice selections, and qualitative ratings without any image data leaving the user's device.
- The tool provides three response types: "noul" (yes/no), choice (pick one from a list), and score (a rating on a specified scale).
- It includes an experimental MLX backend to provide faster runtimes specifically for Apple Silicon users.
- Glance can perform self-calibration using unlabeled data or precise calibration through labeled datasets to improve rating accuracy.
- The software is designed with privacy in mind, ensuring all inference and logging stay local on the user's hardware.
- The tool provides three response types: "noul" (yes/no), choice (pick one from a list), and score (a rating on a specified scale).
- It includes an experimental MLX backend to provide faster runtimes specifically for Apple Silicon users.
- Glance can perform self-calibration using unlabeled data or precise calibration through labeled datasets to improve rating accuracy.
- The software is designed with privacy in mind, ensuring all inference and logging stay local on the user's hardware.
Mohamed Bassem writes about Karakeep, a self-hostable bookmarking application designed for "data hoarders." The app allows users to save links, notes, images, and PDFs with features like automatic metadata fetching, semantic search, LLM-based tagging/summarization (including support for local models via Ollama), and full page archiving. It is built primarily with TypeScript and NextJS, offering cross-platform access through browser extensions, mobile apps, and a web interface.
- Supports local model integration using Ollama for private AI processing
- Includes OCR capabilities to extract text from saved images
- Provides automated video archiving via yt-dlp
- Features full page archival using monolith to prevent link rot
- Supports local model integration using Ollama for private AI processing
- Includes OCR capabilities to extract text from saved images
- Provides automated video archiving via yt-dlp
- Features full page archival using monolith to prevent link rot
Mark Marosi writes about decider, a family of models fine-tuned from Qwen3.5 that produce typed decisions (choice, score, boolean) in a single forward pass without text generation, returning calibrated probability distributions over user-defined options. The project is an open reproduction of TypeSafe AI's "System One" model class (Jev), released in sizes from 0.8B to 35B mixture-of-experts with 3B active parameters.
- The schema cache stores K/V states for repeated question prefixes, achieving up to 19x speedup on large option sets by running only the state per request
- v10 adds calibration-aware RL on live MiniWoB++ browser tasks and exact games, lifting browser accuracy from 83% to 93% and halving the belief gap
- TypeSafe's SDKs work unchanged by pointing TYPESAFE_BASE_URL at the decider server
- The 35B model outperforms the 2B on 93 of 95 regression tasks but costs 3-4x per decision and lacks the RL stage
- Co-developed with Claude (Anthropic) as a listed co-author on commits
- The schema cache stores K/V states for repeated question prefixes, achieving up to 19x speedup on large option sets by running only the state per request
- v10 adds calibration-aware RL on live MiniWoB++ browser tasks and exact games, lifting browser accuracy from 83% to 93% and halving the belief gap
- TypeSafe's SDKs work unchanged by pointing TYPESAFE_BASE_URL at the decider server
- The 35B model outperforms the 2B on 93 of 95 regression tasks but costs 3-4x per decision and lacks the RL stage
- Co-developed with Claude (Anthropic) as a listed co-author on commits
Strands Agents Tools is a community-driven Python package designed to extend the capabilities of LLM agents by providing prebuilt integrations for common tasks. The library bridges the gap between conversation and action, offering tools for file I/O, shell execution, web searching via Tavily or Exa, and complex agentic behaviors like multi-agent coordination and persistent memory. By modularizing these essential functions, it allows developers to avoid reinventing standard plumbing when building practical applications with the Strands Agents SDK.
- Supports various memory backends including Mem0, Amazon Bedrock Knowledge Bases, Elasticsearch, and MongoDB Atlas.
- Includes safety features like user confirmation for Python code execution.
- Enables advanced patterns such as "agent as tool" which allows nesting agents with different models.
- Modular design allows users to install only the specific tools they require via PyPI (`strands-agents-tools`).
- Supports various memory backends including Mem0, Amazon Bedrock Knowledge Bases, Elasticsearch, and MongoDB Atlas.
- Includes safety features like user confirmation for Python code execution.
- Enables advanced patterns such as "agent as tool" which allows nesting agents with different models.
- Modular design allows users to install only the specific tools they require via PyPI (`strands-agents-tools`).
ReadAny is an open-source, privacy-focused e-book reader designed to enhance reading through intelligent chat, semantic search, and knowledge management features. It offers a variety of tools including text-to-speech with over 100 voices, cross-device synchronization via WebDAV or S3, and detailed reading statistics visualized as heatmaps and trend charts.
- Supports multiple formats such as EPUB, PDF, MOBI, AZW, FB2, and CBZ.
- Integrates with various AI providers including OpenAI, Claude, Gemini, Ollama, and DeepSeek.
- Built using Tauri, React, TypeScript, Rust, and SQLite for high performance.
- Allows users to export Markdown notes directly to Obsidian or Notion.
- Supports multiple formats such as EPUB, PDF, MOBI, AZW, FB2, and CBZ.
- Integrates with various AI providers including OpenAI, Claude, Gemini, Ollama, and DeepSeek.
- Built using Tauri, React, TypeScript, Rust, and SQLite for high performance.
- Allows users to export Markdown notes directly to Obsidian or Notion.
Yash Patel writes about his preferred writing workflow, which centers on using Markdown for the actual drafting process to minimize distractions and maintain focus. To meet professional requirements when sharing finished work with clients or editors who require .docx files, he utilizes Pandoc, an open-source document converter, to bridge the gap between simple text files and Microsoft Word documents.
- Pandoc is a free, open-source tool available for installation or via web.
- The basic command line usage for conversion is: `pandoc article.md -o article.docx`
- Markdown allows authors to focus on writing by using symbols instead of formatting buttons.
- Pandoc is a free, open-source tool available for installation or via web.
- The basic command line usage for conversion is: `pandoc article.md -o article.docx`
- Markdown allows authors to focus on writing by using symbols instead of formatting buttons.
Paul Sawers writes that Amazon Web Services (AWS) has released an open-source application called Pizza Bot, which provides developers with an email-inspired inbox to manage autonomous AI agents. Designed specifically for tasks that continue after a user has finished their session, the tool moves away from chat interfaces toward an asynchronous model where completed jobs arrive as threads and urgent decisions are surfaced for human triage.
- The project is now a standalone community project rather than an AWS service.
- It supports multiple models including Anthropic, Amazon Bedrock, Google Gemini, OpenAI, OpenRouter, or local models via Ollama.
- Built using LangGraph and DeepAgents to enable stateful agent execution and persistence through checkpoints.
- Available as a desktop app for macOS, Windows, and Linux, with browser and terminal clients also available.
- The project is now a standalone community project rather than an AWS service.
- It supports multiple models including Anthropic, Amazon Bedrock, Google Gemini, OpenAI, OpenRouter, or local models via Ollama.
- Built using LangGraph and DeepAgents to enable stateful agent execution and persistence through checkpoints.
- Available as a desktop app for macOS, Windows, and Linux, with browser and terminal clients also available.
Erik Elfstrom writes about OrcSDR, an open-source standalone SDR application designed for the M5Stack Tab5 / ESP32-P4 and compatible with the RTL-SDR Blog V4. Unlike traditional setups that require a PC or Raspberry Pi to act as a host, OrcSDR allows the microcontroller itself to handle radio functions, DSP, graphics, audio, storage, and the touchscreen interface. This creates an efficient, low-power, and self-contained portable radio appliance capable of single-channel decoding for various modes such as FM, ADS-B, P25, LoRa, and more.
- The ESP32-P4 handles USB host communication with the RTL-SDR Blog V4 directly.
- Dedicated screens are available for FM, P25, ADS-B, LoRa, RF analysis, and 2.4 GHz Wi-Fi analysis.
- Includes advanced visualization features like I/Q constellation, phosphor persistence, and a 3D spectrum history.
- Features a serial command interface allowing control via scripts or automation in addition to the touchscreen.
- The underlying driver was developed by analyzing USB traffic from known desktop software using Wireshark.
- The ESP32-P4 handles USB host communication with the RTL-SDR Blog V4 directly.
- Dedicated screens are available for FM, P25, ADS-B, LoRa, RF analysis, and 2.4 GHz Wi-Fi analysis.
- Includes advanced visualization features like I/Q constellation, phosphor persistence, and a 3D spectrum history.
- Features a serial command interface allowing control via scripts or automation in addition to the touchscreen.
- The underlying driver was developed by analyzing USB traffic from known desktop software using Wireshark.
