Joe Rice-Jones writes about how he used a local LLM to automate the organization of his cluttered Downloads folder. By connecting a small model with Lemonade to a PowerShell script, he created a two-tiered system where boring rules handle easy tasks like sorting installers by file extension, while an AI (specifically Qwen3.5-9B) handles more complex naming for screenshots and documents via localhost. This setup ensures privacy because all data stays on his machine, avoids the chaos of automated deletions through strict safety protocols, and has resulted in a consistently tidy folder.
- The system uses Lemonade to run models locally on the same PC via an OpenAI-compatible API.
- To prevent errors or loss of important files, the script requires 75% confidence from the model before renaming anything.
- A "safety list" prevents the AI from creating new folders outside of approved directories.
- The process is set as a scheduled task to run once per week.
- The system uses Lemonade to run models locally on the same PC via an OpenAI-compatible API.
- To prevent errors or loss of important files, the script requires 75% confidence from the model before renaming anything.
- A "safety list" prevents the AI from creating new folders outside of approved directories.
- The process is set as a scheduled task to run once per week.
Sara A. Metwalli writes that coding agents differ from basic autocomplete or assistants because they can interact with their environment by examining repositories, modifying files, and running tests in a loop. To use them effectively, developers should provide clear constraints and context rather than just long prompts, following a workflow of inspection, planning, implementation, testing, and review. Instead of delegating massive tasks like rewriting entire applications, users should break work into small, testable problems to maintain control over the architectural integrity and quality of the generated code.
- Agents are best suited for exploratory or multi-step tasks such as finding/fixing bugs or investigating failing tests.
- A recommended prompt structure includes a Goal, Context, Constraints, Acceptance Criteria, and Validation steps.
- The developer's role shifts from writing raw code to directing, assessing, and coordinating complex code-generating systems.
A successful agentic request should include five specific elements rather than just long prompts:
```text
Goal: What you want to achieve »
Context: Specific files or architecture to inspect »
Constraints: Rules/boundaries the agent must follow »
Acceptance criteria: The definition of a "finished" task, e.g., passing tests »
Validation: How to verify success, e.g., run specific test commands »
```
- Agents are best suited for exploratory or multi-step tasks such as finding/fixing bugs or investigating failing tests.
- A recommended prompt structure includes a Goal, Context, Constraints, Acceptance Criteria, and Validation steps.
- The developer's role shifts from writing raw code to directing, assessing, and coordinating complex code-generating systems.
A successful agentic request should include five specific elements rather than just long prompts:
```text
Goal: What you want to achieve »
Context: Specific files or architecture to inspect »
Constraints: Rules/boundaries the agent must follow »
Acceptance criteria: The definition of a "finished" task, e.g., passing tests »
Validation: How to verify success, e.g., run specific test commands »
```
Anurag Singh replaced five Python scripts (backup, organizer, renamer, cleaner, watchdog) with a local LLM agent, which made errors the scripts didn't (wrong directories, skipped steps, false success reports).Each of the original scripts followed explicit rules through a scheduler; the agent instead added a longer inference chain (inspect, interpret, choose a tool, build a command, execute, review) to tasks that fixed logic already described completely, while also holding a loaded model in memory between runs.
- AutomationBench scores for frontier models remain well under 20%: GPT-5.6 Sol 18.1%, GPT-5.5 12.9%, Claude Opus 4.8 15.5%, Gemini 3.5 Flash 14.5%
- Granting an LLM system-level access creates a prompt-injection vector: a malicious file on disk could carry instructions the agent interprets as commands
- Singh's proposed fix: let the agent classify and route ambiguous requests, then hand off to a validator + fixed script for the actual filesystem action
- The five original scripts covered photo backup, extension-based Downloads sorting, file renaming, app-cache clearing, and a disk-threshold alert
- AutomationBench scores for frontier models remain well under 20%: GPT-5.6 Sol 18.1%, GPT-5.5 12.9%, Claude Opus 4.8 15.5%, Gemini 3.5 Flash 14.5%
- Granting an LLM system-level access creates a prompt-injection vector: a malicious file on disk could carry instructions the agent interprets as commands
- Singh's proposed fix: let the agent classify and route ambiguous requests, then hand off to a validator + fixed script for the actual filesystem action
- The five original scripts covered photo backup, extension-based Downloads sorting, file renaming, app-cache clearing, and a disk-threshold alert
Jay writes about a curated collection of over 200 ready-to-import n8n workflows that combine standard automation with modern LLM stacks, including vector databases and embeddings. These templates are designed to facilitate rapid prototyping or production deployment across various sectors such as finance, healthcare, and DevOps through simple JSON imports.
- Supported tech stacks include Pinecone, Weaviate, Supabase Vector, Redis, OpenAI, Anthropic Claude 3, and Hugging Face.
- Workflows can incorporate Slack alerts, Google Sheets logs, OCR, and HTTP polling.
- The repository invites community contributions to complete any unfinished templates.
- Supported tech stacks include Pinecone, Weaviate, Supabase Vector, Redis, OpenAI, Anthropic Claude 3, and Hugging Face.
- Workflows can incorporate Slack alerts, Google Sheets logs, OCR, and HTTP polling.
- The repository invites community contributions to complete any unfinished templates.
Lightpanda is an original headless browser written in Zig, purpose-built for high-performance web automation and LLM-driven agents rather than being a fork of Chromium or WebKit. It prioritizes low memory consumption and execution speed by omitting unnecessary graphical rendering components while maintaining JavaScript support through the V8 engine. The system provides a CDP server to maintain compatibility with existing Puppeteer and Playwright workflows.
- Benchmark data suggests 9x faster execution compared to Headless Chrome.
- Includes an "Agent mode" that outputs deterministic PandaScript from natural language instructions.
- Offers native Model Context Protocol (MCP) support for seamless agent interaction.
- Benchmark data suggests 9x faster execution compared to Headless Chrome.
- Includes an "Agent mode" that outputs deterministic PandaScript from natural language instructions.
- Offers native Model Context Protocol (MCP) support for seamless agent interaction.
Skill Recorder converts screen activity into repeatable agent tasks by recording user sessions and reconstructing intent via GitHub Copilot. Captured data includes clicks, window changes, URLs, and optional narration to produce structured Skills or Automations.
- Built with Electron.
- Performs on-device transcription using Whisper for 99 languages.
- Limits cloud uploads to specific metadata during the analysis phase.
- Built with Electron.
- Performs on-device transcription using Whisper for 99 languages.
- Limits cloud uploads to specific metadata during the analysis phase.
Yanli Liu writes that harness engineering has emerged as a vital discipline where engineers focus on designing environments, feedback loops, and documentation structures for agentic systems rather than writing code manually. This shift enables highly automated development cycles to function reliably by emphasizing the constraints under which these models operate.
- OpenAI's "Harness Engineering" concept sparked this movement in February 2026.
- Anthropic released three separate papers regarding effective harnesses and managed agents.
- Advancements like Opus 4.7 continuously simplify necessary harness components as model capabilities improve.
- OpenAI's "Harness Engineering" concept sparked this movement in February 2026.
- Anthropic released three separate papers regarding effective harnesses and managed agents.
- Advancements like Opus 4.7 continuously simplify necessary harness components as model capabilities improve.
@0xabad1dea@infosec.exchange writes about an incident where AI-assisted mathematical proofs appear to exploit bugs in theorem provers, specifically highlighting a case involving the Collatz conjecture and Lean 4. The discussion explores whether large language models are inadvertently discovering software vulnerabilities through pattern matching or learning from existing technical discussions about those bugs, while broader debates address the inherent limitations of formal verification when facing hardware faults, modeling errors, and human mistakes in specifications.
An author describes the process of replacing various daily browser extensions—such as Grammarly and video summarizers—with a single local Large Language Model (LLM). By setting up Ollama to run models like Qwen or Llama on a local machine, they use extensions like PageAssist to bridge the gap between the model and the web interface. This setup enables advanced document analysis and agentic tasks through automation tools rather than simple one-to-one extension replacements.
- Using Ollama as a backend for browser communication via CORS configuration.
- Running models directly in browsers using WebGPU or WebAssembly.
- Integrating LLMs with Python scripts and workflow automation for custom tasks like price tracking.
- Using Ollama as a backend for browser communication via CORS configuration.
- Running models directly in browsers using WebGPU or WebAssembly.
- Integrating LLMs with Python scripts and workflow automation for custom tasks like price tracking.
This document contains system prompt instructions for an AI model designed to function as a Gmail Assistant. It details specific protocols for managing email threads, including deciding between providing single or multiple reply options based on user input complexity. The instructions cover tone maintenance, strict prohibitions against hallucinating information not present in context, and precise formatting rules for greetings and sign-offs.
* Decision logic for generating one vs three replies
* Guidelines for maintaining professional email etiquette
* Constraints to prevent making up non-existent information
* Rules for extracting and listing action items from threads
* Decision logic for generating one vs three replies
* Guidelines for maintaining professional email etiquette
* Constraints to prevent making up non-existent information
* Rules for extracting and listing action items from threads
