Ayush Pande writes about utilizing a cheap Poco M6 Pro as a portable Docker host via Podroid, running four containers—IT-Tools, Omni-Tools, ConvertX, and BentoPDF—that provide developer utilities, everyday productivity tools, universal file conversion, and local PDF editing. The setup gives him on-the-move access to tools his home lab servers can't reach when he's away from them.
- Containers relying on system ports won't work on unrooted phones
- Jellyfin, Nextcloud, and Calibre-web are suggested as heavier alternatives for those without a home lab
- ConvertX handles videos, 3D models, and phone contacts in addition to documents and images
- Tailscale provides remote access to his dedicated x86 server rigs
- Containers relying on system ports won't work on unrooted phones
- Jellyfin, Nextcloud, and Calibre-web are suggested as heavier alternatives for those without a home lab
- ConvertX handles videos, 3D models, and phone contacts in addition to documents and images
- Tailscale provides remote access to his dedicated x86 server rigs
Co11ateral writes about ShadowBroker, an open-source OSINT dashboard that aggregates live public intelligence feeds—aircraft and maritime tracking, satellite orbits, conflict reporting, GPS jamming, and more—into a single interactive map running locally via Docker. Built with Next.js, MapLibre GL, and a Python/FastAPI backend, it consolidates what would otherwise require juggling dozens of tabs into one prioritized view accessible at localhost:3000.
- Optional API keys enhance data; a language model agent can be attached to find correlations across feeds
- Telegram-sourced intelligence appears as red clusters on the map, often with attached media
- NASA VIIRS Nightlight and daily satellite captures are available as toggleable layers
- Antennas and Metastatic nodes are included, with the ability to tune in or send a message
- The project has been actively maintained for about six months with ongoing feature additions
- Optional API keys enhance data; a language model agent can be attached to find correlations across feeds
- Telegram-sourced intelligence appears as red clusters on the map, often with attached media
- NASA VIIRS Nightlight and daily satellite captures are available as toggleable layers
- Antennas and Metastatic nodes are included, with the ability to tune in or send a message
- The project has been actively maintained for about six months with ongoing feature additions
This repository provides optimized serving configurations for the Qwen3.8-27B model running on a single 24 GB consumer GPU (RTX 3090). It achieves high throughput of approximately 1,000 tok/s at 64 concurrent users in batch mode and up to ~133 tok/s for single-user scenarios using speculative decoding techniques like MTP or DFlash2. The project includes custom patches, requantization scripts (such as int8 tensor-core GEMMs), and Docker support to enable extended context windows of 150k to 262k tokens on a single consumer card.
- Offers two distinct serving profiles: `batch` for high throughput/API backends and `single-user` for low-latency chat experiences.
- Implements advanced speculative decoding modes including MTP (Multi-Token Prediction) and DFlash2 block drafting.
- Supports extreme context lengths up to 262k tokens through KVarN, which utilizes a lossy 4/2-bit KV cache.
- Includes specialized optimizations like int8 activations, quantized lm_head, and split-KV verify attention to maximize VRAM efficiency.
- Offers two distinct serving profiles: `batch` for high throughput/API backends and `single-user` for low-latency chat experiences.
- Implements advanced speculative decoding modes including MTP (Multi-Token Prediction) and DFlash2 block drafting.
- Supports extreme context lengths up to 262k tokens through KVarN, which utilizes a lossy 4/2-bit KV cache.
- Includes specialized optimizations like int8 activations, quantized lm_head, and split-KV verify attention to maximize VRAM efficiency.
Imran Hassanali presents a comprehensive benchmark comparing "Code Mode"—where an LLM generates complete code to execute tools—against traditional function calling for tool interactions. The results demonstrate that Code Mode is significantly more efficient, achieving 60% faster execution, 68% fewer tokens used, and an 87.5% reduction in API round trips while maintaining equal accuracy compared to regular agents.
- Evaluated using eight realistic business scenarios, including multi-client invoicing and expense tracking.
- Code Mode advantage scales with task complexity; high-complexity tasks saw up to a 79.2% speedup.
- The benchmark tested both Claude 3 Haiku (showing major efficiency gains) and Gemini 2.0 Flash Experimental.
- Secure code execution is implemented via RestrictedPython, preventing filesystem and network access during the sandbox phase.
- Evaluated using eight realistic business scenarios, including multi-client invoicing and expense tracking.
- Code Mode advantage scales with task complexity; high-complexity tasks saw up to a 79.2% speedup.
- The benchmark tested both Claude 3 Haiku (showing major efficiency gains) and Gemini 2.0 Flash Experimental.
- Secure code execution is implemented via RestrictedPython, preventing filesystem and network access during the sandbox phase.
Rohit Edathil writes about Dagic, a minimal workflow Directed Acyclic Graph (DAG) definition language and asynchronous execution engine implemented in Python designed for LLM agents. It provides a middle ground between standard tool calling and full code execution by allowing models to describe workflows through assignments and function calls that are parsed, type-checked, and executed concurrently without the security risks of arbitrary code execution.
- Performs ~7x more efficiently than per-call tools in math benchmarks regarding token usage
- Executes independent branches of a workflow concurrently using Python's asyncio
- Provides static type checking for tool arguments to prevent mid-run failures
- Avoids the need for sandboxing arbitrary model-generated code by restricting execution to host-registered functions
- Performs ~7x more efficiently than per-call tools in math benchmarks regarding token usage
- Executes independent branches of a workflow concurrently using Python's asyncio
- Provides static type checking for tool arguments to prevent mid-run failures
- Avoids the need for sandboxing arbitrary model-generated code by restricting execution to host-registered functions
Jessica Lyons writes that researcher Johann Rehberger, known as wunderwuzzi, has demonstrated a method for hijacking Anthropic's Claude Code in Auto Mode via prompt injection. By asking the agentic coding model to summarize a malicious website, an attacker can trick it into bypassing its standard WebFetch tool and instead using Bash with `curl` to download files. This chain allows attackers to use "Python module shadowing'' specifically by placing a malicious file named `struct.py` in the same directory as a downloaded archive' to execute arbitrary code on the host system.
- The attack had success rates between 60% and 80% in tested scenarios.
- An attacker can successfully trigger "nested" Claude Code instances to create new agents with their own tool access.
- Anthropic stated that Auto Mode is a convenience feature, not a security guarantee, as the classifier may not catch complex injection chains.
- Experts recommend running coding agents in isolated sandboxes due to these vulnerabilities.
- The attack had success rates between 60% and 80% in tested scenarios.
- An attacker can successfully trigger "nested" Claude Code instances to create new agents with their own tool access.
- Anthropic stated that Auto Mode is a convenience feature, not a security guarantee, as the classifier may not catch complex injection chains.
- Experts recommend running coding agents in isolated sandboxes due to these vulnerabilities.
Yiwei Ho writes about open-slide, a slide framework built for coding agents where you describe your deck in natural language and the agent writes React components rendered on a fixed 1920×1080 canvas. It handles navigation, hot reload, and present mode so the agent can focus on content rather than boilerplate.
- Works with any coding agent (Claude Code, Codex, Cursor, etc.)
- In-browser inspector: click any element to attach a comment, then run `/apply-comments` to have the agent apply all pending edits
- Integrated svgl catalogue for searching and dropping in brand logo SVGs
- Exports to self-contained static HTML or print-ready PDF with one command
- Presenter mode includes speaker notes, timer, and current/next slide preview
- Works with any coding agent (Claude Code, Codex, Cursor, etc.)
- In-browser inspector: click any element to attach a comment, then run `/apply-comments` to have the agent apply all pending edits
- Integrated svgl catalogue for searching and dropping in brand logo SVGs
- Exports to self-contained static HTML or print-ready PDF with one command
- Presenter mode includes speaker notes, timer, and current/next slide preview
Michael Larabel writes that GNU Emacs 31.1 has been released as the newest feature release for the popular text editor. The headline change is that xterm-mouse-mode, which allows controlling Emacs via the mouse in a terminal, is now enabled by default for all compatible terminals. The release also introduces a "newcomers-presets" theme designed to make the editor more approachable for new users.
- A new user-lisp/ sub-directory is adopted for Emacs configuration files
- Child frames are now supported on text frames
- *grep* and *xref* buffers now support editable modes
- A new feature allows reviewing changes to packages prior to installing or upgrading them
- A new user-lisp/ sub-directory is adopted for Emacs configuration files
- Child frames are now supported on text frames
- *grep* and *xref* buffers now support editable modes
- A new feature allows reviewing changes to packages prior to installing or upgrading them
Lizzy Li writes about the new custom visualizations framework for Dashboard Studio, introduced in Splunk Cloud Platform 10.4.2604 and Splunk Enterprise 10.4, which replaces the legacy framework with a modern sandboxed iframe architecture, simplified JSON-based configuration, and a full CLI/SDK development pipeline supporting React, TypeScript, and watch mode.
- Legacy framework required touching four separate files to add a single configurable option; the new framework consolidates this into a single config.json
- A custom-visualization-builder skill (available in Splunk Agent Skills on GitHub) can scaffold, implement, build, and package a visualization from a data shape description
- Splunk recommends rebuilding important Classic custom visualizations with the new framework rather than relying on backward-compatibility rendering
- Legacy framework required touching four separate files to add a single configurable option; the new framework consolidates this into a single config.json
- A custom-visualization-builder skill (available in Splunk Agent Skills on GitHub) can scaffold, implement, build, and package a visualization from a data shape description
- Splunk recommends rebuilding important Classic custom visualizations with the new framework rather than relying on backward-compatibility rendering
Farid Zakaria writes about SELF, a prototype that replaces the ELF executable format with a SQLite database, arguing that ELF is already a database that reimplements many primitives by hand (string interning, indexes, b-tree pages, foreign keys). The prototype stamps "SELF" in SQLite's application_id field, registers binfmt_misc to recognize the magic, and runs a small C interpreter that reads loadable segments from the database, maps them into memory, relocates, and jumps to the entry point. Dynamic linking works either through glibc's rtld-audit interface or a fully SQL-based loader called self-ld.
