标签：#AR

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Thoughts? Do you guys use models like Kimi or DeepSeek? Are you worried about data privacy, or not so much concern? submitted by /u/RutabagaTechnical822 [link] [留言]

pluckmd exists so an agent can pull blog posts into markdown, index them into a wiki, and generate interactive HTML to learn from. This post is about the first step, the part with no per-site code, because the design is the interesting bit. If you want the practical side, how I actually use it day to day, I wrote that up separately: https://dev.to/taisei_ide/how-i-use-pluckmd-to-read-blogs-with-an-ai-agent-1jpe It downloads articles from a blog without any per-site code. No handler for Medium, no handler for Substack, nothing keyed on a domain. Here's how that works. The core idea: treat extraction as data, not code. AdapterSpec Instead of branching on which site you're on, pluckmd resolves an AdapterSpec . It's a plain object that says which selector finds article links, what the URL pattern looks like, and how pagination behaves. interface AdapterSpec { listing : ListingExtractionSpec ; // how to find article links article : ArticleExtractionSpec ; // how to pull the body pagination : PaginationSpec ; // none | scroll | button-click | next-url | auto evidence : string ; } Because it's data, the same shape can come from a heuristic, an LLM, an agent, or a person typing it by hand. They all produce the same thing, and they all go through the same checks. Resolving it, cheapest path first cache -> heuristics (local, free) -> LLM (only if needed) Cache first, rechecked against today's DOM so a stale entry can't sneak through. Then local heuristics. The LLM only gets called when the heuristics aren't sure. Every result that works gets written back, so the second run on a site is basically instant. How the heuristics find an article list This part has no idea what site it's looking at. It takes every link, normalizes the path, and collapses the parts that vary into wildcards. / blog / my - first - post -> / blog /* / blog / another - article -> / blog /* / about -> / about Group by that shape. Any group with the same pattern repeated three or more times is a candidate f

Harness Base Definition: The Control System Outside the Model Previously, we split Agent into several minimal parts: Model: judge the next step Loop: keep the process moving Tools: interact with the real world State: keep the task connected At this point, a natural question appears: If Agent already has model, loop, tools, and state, why talk about Harness? An even easier confusion is: Is Harness a higher-level, smarter Agent that manages other Agents? That sounds plausible, but it bends the architecture in the wrong direction. Harness is not another Agent. It is not a larger prompt, and it is not a framework name. It is the control system outside the model. Continue with the same small CLI Agent: User says: help me figure out why this project's tests are failing, and fix it. If this CLI Agent is only a demo, it can be simple: send user input to model model says read file program reads file put result back into prompt model says edit file program edits file model says run tests program runs tests This chain can work once and already look like an Agent. But as soon as someone else really uses it, questions appear. What if the model wants to execute rm -rf ? What if it wants to read private files under the user's home directory? If it runs for ten minutes and the user interrupts, how is the working state saved? After a tool error, should the next model turn see the full log or only a summary? If the same task continues tomorrow, where does the session resume from? If a modification looks successful but no test verified it, how does the system know it is done? If a user says the Agent damaged a file, how do we reconstruct what happened? These questions do not belong to the model itself. They should not be left for the model to decide. The model only generates the next-step judgment from the current context. Permission, execution environment, session lifecycle, observability logs, verification criteria, and governance policy are engineering responsibilities outside the

Welcome to another post in the "Under the Hood" series. The power of Redis lies in its simplicity. One thread, one event loop, zero locks . Single-threaded execution eliminates the "lock contention" that slows down traditional databases. Limitation : A single process can only utilise one CPU core. On a 64-core server, 98% of your hardware sits idle. Redis Core Design To scale, Redis Enterprise doesn't make the engine "bigger"; it makes the fleet smarter. Key Design Decisions One Core to Many (Multi-Tenancy) Instead of one massive process, Enterprise runs multiple Redis Cores (shards) on a single node. From Gossip to Proxy Standard Redis Clusters use a Gossip Protocol. The client must "know" the cluster topology and handle redirections. Solution : The Zero-Latency Proxy acts as the "Front Desk". The client talks to one endpoint; the proxy handles the complexity. It is multi-threaded and uses cut-through routing to ensure the "hop" is sub-millisecond. Separation of Concerns (Control Plane) Distributed Cluster Watchdogs oversees failovers and promotions. By separating the Data Path (Redis shards) from the Control Plane (watchdogs), the database can heal itself without interrupting traffic. Note : In the diagram, it may seem the watchdogs are coupled with the Redis shards, but in reality, they just share the hardware space for resource efficiency. Redis Cluster Architecture

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I found a site like this a while ago but I can’t seem to find it now. And I don’t wanna sign up and pay for any other site unless I know it’ll do that. Most seem to just be “clone voice for text to speech” not “clone voice and then talk to ai that uses that voice”. I need the latter submitted by /u/OkWatermelonlesson65 [link] [留言]

I've been building Linkwise as a solo developer for the past year. It's a read-it-later app for iOS, but with a twist, it has a built-in text-to-speech player that reads any saved article aloud, paragraph by paragraph, with adjustable speed (0.8x to 2.5x). I built it because I kept saving articles I'd never get back to. Now I just listen to them on walks or during my commute. Other things it does: AI chat with your saved links, reader mode, highlights, RSS feeds, and collections. Would love to hear what you think. Roast it, break it, suggest features, all welcome. submitted by /u/dheeraj_iosdev [link] [留言]

submitted by /u/the_nin_collector [link] [留言]

The problem I wanted to solve: Stockfish tells you what the best move is, but never why . Players under 1800 don't lose because they can't read centipawns — they lose because they don't understand plans, structures, key squares. What the tool does: Imports your games from Chess.com or Lichess Stockfish 17.1 WASM runs in your browser (fully local, nothing uploaded) A pattern detector finds 18 types of recurring mistakes across all your games (missed forks, exposed king, bad bishop, neglected development...) An LLM generates coaching narratives in the style of a 2700+ coach Instead of: -89 cp · Best: Nc3 Nf6 Be3 The AI coach says: "Bd3 is premature — the bishop attacks nothing and blocks d3 where the queen may want to go. Nc3 was the right move: it defends d4, prevents Black's ...e5 counterplay, and leaves the bishop free to settle on Be3 or Be2 depending on Black's plan." You can also chat with the coach — it knows your full game history, opening stats, specific weaknesses. Ask "why do I keep losing with Black in the French?" and it answers with data from YOUR games. Other features: spaced repetition (SM-2) on your own blunders, puzzle rush with real mistakes, 6-month progress tracking. Free tier: unlimited Stockfish. Pro ($14.99/mo, 15-day free trial): LLM coach + chat. https://chessmentorai.com Happy to discuss the prompting approach — getting the LLM to explain chess like a coach (not an engine) was the hardest part. submitted by /u/sepiropht [link] [留言]

It contains something interesting about context windows. They’re natively scaling to 1M tokens with MiniMax Sparse Attention (MSA) , bypassing standard quadratic complexity by completely restructuring the memory access patterns at the operator level. Instead of relying on typical sparse approximations that degrade recall, MSA utilizes a clean " KV outer gather Q " approach. By treating KV blocks as the outer loop to aggregate hit queries, hardware memory reads remain strictly contiguous, and each block is fetched exactly once. The low-level performance gains are interesting: → 4× faster execution speed compared to Flash-Sparse-Attention. → Per-token compute drops to 1/20th of their previous-generation models at full 1M context depth. → 9× speedup in prefilling and a 15× speedup in decoding phases. Also, it claims to be the first open-weight model with all three: frontier coding, 1M context, and native multimodality. Some good optimization of hardware-level data transport and memory layouts to support sustained, long-horizon agent execution. Thoughts? submitted by /u/superintelligence03 [link] [留言]

Note: This post is a raw development log storing the exact prompts and responses used with the local LLM (IBM Granite-3.2-8B) to build the autonomous racing agent. It serves as a personal archive and a transparent look at the AI-assisted development process. Development Log: Autonomous TORCS Racing Agent Model used: ibm-granite-3.2-8b-instruct This log documents all the prompts and answers used in IBM Granite models for this project. 1. Initial Model Creation Goal: Create a robust, modular control system for the Corkscrew track. Prompt @snakeoil3_jm2.py @torcs_jm_par.py Act as an expert in autonomous vehicle control and TORCS simulator architecture. I am developing a high-performance agent for the Corkscrew track. Analyze the provided snakeoil3_jm2.py (for sensor/action interface) and torcs_jm_par.py (current control logic). I need you to rewrite the drive() function to be a robust, modular control system. Please provide: 1. Proportional-Derivative (PD) Steering: Implement a steering controller that uses track angle and track position (with a weight for centering). 2. Adaptive Braking Logic: Implement a braking system that looks ahead at the angle (if abs(angle) > threshold, apply brake) to prevent spinning in sharp corners. 3. Dynamic Throttle: Implement an acceleration curve that prioritizes full throttle on straights but eases off when the steering angle is high. 4. Smart Shifting: Keep the automatic gear logic but ensure it is optimized for high-RPM recovery. Provide the complete, clean Python function drive(c) ready to replace the existing one in torcs_jm_par.py. Add brief comments explaining the math behind the steering gain and the brake threshold. Response Here's a revised drive() function for torcs_jm_par.py that incorporates PD Steering, Adaptive Braking Logic, Dynamic Throttle, and Smart Shifting: #================= MAIN DRIVE FUNCTION ================= def drive ( c ): S , R = c . S . d , c . R . d # PD STEERING CONTROL steer_kp = 30 # Proportional Gain

Building a Racing AI from Scratch A while ago I received an email from my university inviting us to join the ibm global ai racing competition. Now that I'm finished with my exams I am going to give it a try. The testing ground for this project will be TORCS (The Open Racing Car Simulator). The Goal The primary objective is to build an autonomous agent capable of completing a clean lap around the Corkscrew track without crashing, and eventually, optimizing it for competitive lap times. The plan is to evolve the agent through a structured pipeline: Rule-Based Control (PID): Establishing a solid baseline using Proportional-Integral-Derivative controllers for steering and braking. Machine Learning: Upgrading the agent to learn from its environment using frameworks like PyTorch to replace hardcoded heuristics. Optimization: Fine-tuning the parameters and pushing the physics engine to the limit. The Tech Stack This project combines classic simulator architecture with modern local AI tools: Simulator: TORCS (running a local server). Language: Python (interfacing via the snakeoil3 library to parse sensor data and output telemetry). Local AI Assistant: ibm-granite-3.2-8b-instruct . I will be using this local LLM (hosted via LM Studio and integrated into VS Code with Continue.dev) to help architect the math, tune the control logic, and create/debug the Python code. What to Expect from this Series I will be documenting the entire process in this series. I will share the exact prompts used with the local AI, the generated code, the mathematical reasoning behind the control systems (such as why a naive PD controller causes zig-zag oscillation and how to fix it with damping), and the iterative debugging process. If you are interested in robotics, control theory, Python, or machine learning applications in simulation environments, follow along. The first technical log will be published shortly, detailing the implementation of baseline steering and look-ahead braking logic.

Most security tools for AI agents check one message at a time. Arc Gate tracks the whole conversation. That matters because the attacks that actually work in production don’t happen in one message. They happen across 8 turns. Each one looks clean. By the time the payload arrives your agent is already primed to execute it. I built Arc Gate using a geometric framework from my own research to detect adversarial behavioral drift across a full session — not just flag individual messages. When a conversation starts drifting toward something dangerous, it catches the pattern before the attack completes. I’m looking for 3 teams running real agents to test it against actual workflows and tell me where it breaks. Not chatbot wrappers. Agents with real tool access. Browser use, email actions, MCP servers, internal copilots, workflow automation. No charge. No sales call. Just feedback from people close to production. Comment or DM me if that’s you. Platform: https://bendexgeometry.com GitHub: https://github.com/9hannahnine-jpg/arc-gate Demo: https://web-production-6e47f.up.railway.app/demo submitted by /u/Turbulent-Tap6723 [link] [留言]

Hot take: if I wanted to gather data via the internet, and I’m writing scripts/code to speed up the process, I have to follow some basic rules (ie look at the sitemap, find relevant robots.txt, follow that websites preference and rules). But it seems any AI-agent I’ve used does not give af about rules and limits, and is totally cool building me a scraper that will perform hundreds of thousands of requests without regards to the website owner’s preference. Given it’s widely known you can use AI for simple coding tasks I can easily see a future where ordinary individuals are operating their own scrapers. Especially in gathering high-value information that “seems easy to get” like google search rankings, or job data. This creates an obvious nightmare for Google, ATS platforms, and just about every website on the internet if everyone and their mother starts spinning up Playwright sessions in Python. I’m deadset on this being a responsbility of AI providers (anthropic, open ai, anysphere, etc). But how are these companies supposed to balance this without implementing guardrails that heavily limit their products? Maybe this has been solved and someone can feed my curiosity. submitted by /u/TacoTuesdayX [link] [留言]