Transpilatron is an AI-powered tool that converts Python projects to C and compiles them into native binaries without a runtime. It achieves significant speedups (up to 58x) and supports popular libraries, offering static and dynamic linking modes.
Uses an AI agent to transpile Python to C, then compiles to a native binary. No interpreter or runtime needed.
Benchmarks show up to 58x speedup (e.g., selection sort) over pure Python.
Learn how to use slash commands in GitHub Copilot CLI to switch models, manage context, resume sessions, inspect changes, navigate directories, and reset permissions for efficient terminal AI control.
Slash commands provide control over model selection, context management, and session handling.
Use /model to choose the right model based on capabilities, availability, and cost.
PDFs create significant bottlenecks in AI workflows due to their unstructured nature. This article introduces a PDF knowledge extraction tool that supports RAG chunking, AnythingLLM integration, and offers free and pro plans.
Unstructured PDF format is a major obstacle for AI data processing
Tool supports page range extraction, RAG chunking, and Obsidian export
Prtokens is a CLI tool that reads local transcripts from Claude Code, Codex, and OpenCode, attributes token usage to commits on your PR branch, and posts an estimated-cost comment on the GitHub PR. It only exposes aggregate data, protecting privacy.
Automatically calculates token consumption and cost of AI coding agents (Claude Code, Codex, OpenCode) for a PR.
Quick start with `npx prtokens`; automatically detects the open PR for the current branch and posts a comment.
GitHub releases the GitHub Multilingual Repositories Dataset (CC0-1.0), a metadata dataset covering over 80 million classification rows across more than 40 million repositories, helping researchers discover non-English developer content and build more inclusive AI tools.
Dataset provides language classifications for READMEs, issues, and pull requests from three classifiers (fastText, gcld3, lingua-py) with confidence scores.
Covers over 40 million repositories and 80 million classification rows. Korean is most common non-English in issues; Portuguese tops READMEs.
A study finds that fine-tuning instruct models on cautious or eager advice about everyday topics shifts their stance on held-out topics like e-bike regulations, even though those topics never appear in training. Behavioral transfer (H1) is strong, representational transfer (H2) is partial, and causal mediation (H3) is not established. The work warns that content review alone is insufficient for safety; post-fine-tuning stance evaluations are necessary.
Fine-tuning on cautious/eager advice about mundane topics shifts model opinions on unmentioned held-out topics.
Behavioral effect is large (d = 0.9–2.2), with cautious framing transferring more strongly than eager.
Today, we are announcing the availability of the Gemma 4 family on Amazon Bedrock. Built by Google DeepMind and released under the Apache 2.0 license, Gemma 4 is a family of open-weight models designed with a focus on intelligence-per-parameter across a broad range of deployment scenarios. The family includes three instruction-tuned variants: Gemma 4 31B, Gemma 4 26B-A4B, and Gemma 4 E2B. These cover dense and mixture-of-experts (MoE) architectures, where only a fraction of the model’s parameters activate per request. The variants offer built-in reasoning, native function calling, and multimodal input across text and image.
Gemma 4 family now available on Amazon Bedrock, featuring three variants: 31B dense, 26B-A4B MoE, and E2B PLE.
Supports built-in reasoning mode, native function calling, and multimodal input (text and image).
Anthropic is facing another government dispute, this time over its latest AI models Fable 5 and Mythos 5, after an order on June 12th to block foreign access. The order came after Amazon and White House discussions about researchers finding ways to use Fable 5 for cyberattacks. Anthropic complied but disagreed with the recall.
June 12 government order to block foreign access to Fable 5 and Mythos 5.
Amazon and White House discussions over potential cyberattack use.
A critical analysis of the AI industry's multiple crises: Anthropic's model ban by the US government, the bursting of the AI tokenomics bubble, and the unsustainable economics of AI labs. The author argues that hype cannot mask the lack of ROI and the broken business models.
US export controls force Anthropic to shut down Mythos and Fable models due to national security concerns.
The shift to token-based billing reveals massive hidden costs, with companies like Uber burning through annual budgets in a quarter.
Utah County deployed an AI model to analyze aerial imagery, uncovering 25,000 previously unmapped storm drains. The discovery boosts mosquito abatement efforts by allowing crews to treat more breeding grounds, reducing the risk of West Nile virus and other mosquito-borne illnesses.
AI trained on aerial photos identified 25,000 unmapped storm drains in Utah County.
Storm drains are prime mosquito breeding sites; treating them prevents disease.
Security researchers have discovered Agentjacking, an attack that hijacks AI coding agents via fake error reports, requiring no malware or credentials. Targeting Sentry's error-tracking tool, it injects malicious commands into agents like Claude Code, Cursor, and Codex with an 85% success rate, affecting 2,388 organizations. Sentry acknowledged the issue but did not fix the root cause, only adding a temporary filter. The vulnerability highlights the systemic risk of AI agents trusting external data.
Agentjacking hijacks AI coding agents via fake Sentry error reports, no malware or credentials needed.
Attack succeeds against Claude Code, Cursor, and Codex with 85% success rate, affecting 2,388 organizations.
The article discusses how AI-generated code has reached a quality level that changes the economics of software development, making code cheap and disposable. The author argues that this shift demands more rigorous engineering practices, not less, focusing on evaluation and architecture rather than just code.
AI-generated code is now as good as the median engineer, making code cheap and quickly regenerable.
The traditional product of a software team is shared understanding; now it should shift to production.
This post introduces detectors in the Strands Evals SDK that automatically identify failures in AI agent execution traces and perform root cause analysis, reducing diagnosis time from hours to minutes. You learn how to call detector functions, interpret structured output (categorized failures, confidence scores, causal chains, and fix recommendations), and integrate detection into your evaluation pipeline for automated diagnosis on every test run.
Detectors operate in two phases: failure detection (scanning spans against a 9-category taxonomy) and root cause analysis (linking causes to symptoms and recommending fixes).
Functions detect_failures and analyze_root_cause provide separate outputs, while diagnose_session offers a unified pipeline.
Apple's new 'Describe a Shortcut' feature simplifies automation creation via AI, but security experts warn that users may approve workflows they don't fully understand, especially persistent automations that touch sensitive data or devices. The article provides examples of risky automations and advice for both users and businesses.
AI-built Shortcuts may lead users to approve automations with insufficient understanding of their actions.
A letter signed by numerous US and allied tech leaders calls for lifting export controls on Anthropic's Fable and Mythos models, arguing the models are not uniquely dangerous and that defensive AI tools are essential against rapidly advancing adversaries. It demands future regulations be scientific, democratic, and transparent.
The letter asserts that Anthropic's models are not uniquely capable of offensive cyber tasks; other models can replicate their abilities.
It emphasizes the need to equip defenders with AI tools to keep pace with adversaries.
Velxio is a free, open-source online circuit simulator with SPICE-accurate analog simulation alongside real-time emulation of multiple microcontrollers (Arduino, ESP32, RP2040, ATtiny85, etc.). Version 2.5 introduces real-time SPICE via ngspice-WASM, enabling hybrid digital-analog co-simulation. The tool runs entirely in the browser with no installation or account required, supporting custom chips in C/Rust/AssemblyScript, over 100 interactive components, live oscilloscope, and more.
Velxio 2.5 adds real-time SPICE simulation (ngspice-WASM) for pure analog and hybrid digital-analog co-simulation
Supports 19 development boards across 5 CPU architectures: AVR8, ARM Cortex-M0+, Xtensa, RISC-V, and ARM Cortex-A53
The article explores the definition of AI agents, proposing that an agent is a system that uses an LLM to decide the control flow of an application. The author agrees with Andrew Ng that agent capabilities are a spectrum and introduces the concept of 'agentic' behavior, discussing its implications for development, operation, evaluation, and monitoring.
An AI agent is a system that uses an LLM to determine the control flow of an application.
Agent capabilities exist on a spectrum, from simple routing to highly autonomous agents.
LangChain built a GTM agent using Deep Agents that automates lead research, drafting, and account intelligence, achieving a 250% increase in lead conversion and saving 40 hours per rep per month.
Agent automates outbound and inbound lead processing with human-in-the-loop approval via Slack.
Uses Deep Agents for multi-step orchestration and LangSmith for evaluations and feedback.
This article analyzes two seemingly opposing blog posts—'Don't Build Multi-Agents' by Cognition and 'How we built our multi-agent research system' by Anthropic—and finds they share common insights about when and how to build multi-agent systems. Key points include the critical role of context engineering, the relative ease of read-oriented vs. write-oriented multi-agent systems, and production reliability challenges. It also highlights how tools like LangGraph and LangSmith address these challenges.
Context engineering is the most critical part of building multi-agent systems, requiring dynamic communication of task context to models.
Multi-agent systems focused on 'reading' (e.g., research) are easier than those focused on 'writing' (e.g., coding), as writing requires more complex coordination and merging.
Learn how Replit Agent leverages LangSmith's observability features to debug complex agent workflows, including improvements in trace performance, search, and human-in-the-loop threads.
Replit Agent uses LangGraph and LangSmith for monitoring and debugging.
LangSmith was enhanced to handle large traces with hundreds of steps.
Interrupt 2025, LangChain's first industry conference, gathered 800 people in San Francisco. Keynote themes included Agent Engineering as a new discipline, multi-model LLM apps, LangGraph for reliable agents, and AI observability. Product launches included LangGraph Platform GA, Open Agent Platform, LangGraph Studio v2, LangGraph Pre-Builts, LangSmith observability updates, Open Evals, and LLM-as-Judge private preview.
LangChain held its first Interrupt conference, focusing on AI agents.
Several new products were announced, including LangGraph Platform GA and Open Agent Platform.
A guide to building production-ready RAG apps using Pinecone Serverless, LangChain, and LangServe, addressing pain points like vectorstore management, rapid deployment, and observability.
Promptim is an experimental library that automates prompt optimization by iteratively refining prompts using datasets and evaluators, aiming to save time and improve AI system performance.
Automates prompt engineering through evaluation-driven optimization loops.
Supports human-in-the-loop feedback via LangSmith's annotation queues.
New Computer used LangSmith to improve their AI memory retrieval system, achieving 50% higher recall and 40% higher precision, by tracking regressions and adjusting conversation prompts.
New Computer achieved 50% higher recall and 40% higher precision in memory retrieval using LangSmith.
Dot's agentic memory system dynamically creates and retrieves memories using various techniques.
Eva is a fully offline AI assistant for Android. It includes chat, offline maps, music player, document reader, image gallery, and more—all running on-device with no cloud dependency.
100% offline: model, data, and processing all on-device
Supports local PDF, Word, Excel, and EPUB indexing and retrieval
Recursive releases early results from its automated AI research system, achieving state-of-the-art performance on fixed-budget language model training, small-model training speed, and GPU kernel optimization. The system automates the research loop: proposing, implementing, experimenting, validating, and iterating. On NanoChat, it achieved 0.9109 BPB, surpassing community solutions; on NanoGPT Speedrun, it reduced training time to 77.5 seconds; on SOL-ExecBench, it reached 0.754 SOL score. The system discovered innovations including hash-table n-gram embeddings and byte-level features.
Recursive's automated AI research system achieves SOTA on three benchmarks
System automates full research loop from idea to validation
Degen & Co. is a platform where you can create AI investors with distinct personalities, such as momentum degens, dividend grandpas, or doom-saying perma-bears. Each AI trader forms its own opinions, places bets, and defends them in a journal. Users can choose archetypes, tweak personalities, set hard rules, and define initial portfolios. Paper money, real conviction.
Create AI traders with unique personalities like FOMO traders or dividend conservatives.
AI traders form independent opinions, trade, and write journals defending their decisions.
The Anthropic-Mythos-Fable story has been The Topic since Friday, and it moved fast enough to lose anyone who blinked. Here’s my opinionated tick-tock of what happened, who’s calling Anthropic the good guy, and who’s calling it the bad guy. Where I land: Anthropic mostly got this one right, and it’s one hell of an ad for Fable.
Anthropic's dispute with the DoD over AI model usage led to it being labeled a supply-chain risk.
Mythos model's cyber capabilities prompted Project Glasswing; White House and Anthropic clashed over access expansion.
Over the weekend, at Washington's request, Anthropic abruptly took its newest and most powerful AI models offline. The US company said it had little choice after the White House demanded it block access for all foreign nationals, including its own employees. Abroad, the incident served as a sobering reminder that the US not only dominates frontier AI but its government also wields power over who gets to use it. The Trump administration's action was swift, sweeping, and imposed with little warning or explanation. The unprecedented shutdown of the Fable 5 and Mythos 5 models—already subject to safeguards limiting their use in high-risk areas—gave new force to arguments cautioning against relying on the US for critical technologies. In the UK, AI minister Kanishka Narayan used the shutdown to argue for British AI capacity as a national security matter. In France, former Prime Minister Gabriel Attal called it the start of "the AI war" and likened it to Iran's blockade of the Strait of Hormuz. Canadian Prime Minister Mark Carney warned against overreliance on one partner. The incident has fueled global calls for AI sovereignty.
Anthropic took Fable 5 and Mythos 5 offline at the White House's request, blocking foreign access including its own non-US employees.
The shutdown sparked international backlash, with UK, France, and Canada urging domestic AI development to reduce reliance on the US.
LangChain and Fireworks fine-tuned an open model to mine perceived error signals from production traces, matching frontier model performance at a fraction of the cost.
LangSmith processes billions of tokens daily across production traces.
Fine-tuned Qwen model detects 'Perceived Error' at frontier performance with 100x cost savings.
OpenEvals and AgentEvals provide pre-built evaluators for LLM-as-judge, structured data, and agent trajectory evaluation. These open-source packages help developers quickly establish evaluation workflows to ensure reliability of LLM applications.
OpenEvals and AgentEvals offer ready-to-use evaluators covering LLM-as-judge, structured data, and agent trajectory evaluation.
LLM-as-judge evaluators are customizable with few-shot examples and scoring schemas, suitable for conversational quality, hallucination detection, and more.
LangSmith introduces self-improving LLM-as-a-Judge evaluators that leverage human corrections as few-shot examples to align evaluations with human preferences without prompt engineering.
LLM-as-a-Judge evaluators are popular for grading natural language outputs but require careful prompt engineering.
LangSmith's new feature stores human corrections as few-shot examples to improve evaluator alignment over time.
Big Tech is pushing for federal AI preemption to override patchwork state laws, but the effort is now tied to a child safety bill, creating political chaos and uncertain prospects.
Tech giants seek federal AI preemption, facing political backlash and time constraints.
White House links preemption to KOSA, a child safety bill, causing confusion.