Hey , I've been building VeritasReason — an open-source Python framework that adds a structured reasoning and provenance layer on top of LLMs and AI agents. The problem it solves: AI agents today make decisions but record nothing. When something breaks in prod, you have zero audit trail. What it does: • Context Graphs — queryable graph of everything your agent knows + decides • Forward-chaining rule engine (YAML rules, no code required) • W3C PROV-O provenance — every answer traces back to its source fact • Policy compliance: ask "Which purchase orders violated SoD policy in Q1?" • Works with OpenAI, Anthropic, Groq, Ollama, any LLM 30-second demo: pip install veritas-reason veritasreason-policy-demo GitHub: https://github.com/bibinprathap/VeritasGraph PyPI: https://pypi.org/project/veritas-reason/ Happy to answer questions — built this for regulated-industry AI (healthcare, finance, legal) where "trust me bro" answers aren't enough. — Bibin submitted by /u/BitterHouse8234 [link] [留言]

The same agent, same prompts, did markedly different work on two codebases I work in. One has a test suite that runs in eight seconds. The other takes twelve minutes. The eight-second project gets a careful, iterative collaborator. The twelve-minute project gets a confident guesser. I noticed it first as a vibe. The agent in the slow codebase would write five files at once, then announce the task complete without having run anything end to end. The agent in the fast codebase would write one function, run the tests, react to the failure, fix it, run them again. Same model. Same configuration. The only difference was how expensive it was to learn whether the previous step was right. That is the whole post in one sentence. An agent's effective intelligence is bounded by how fast it can verify its hypotheses. Cut the verification cost and you raise the agent's apparent IQ. Raise it and you lower the agent's apparent IQ. The model in the middle is unchanged. Why this binds harder for agents than for humans A human engineer can hold a hypothesis in their head. "I think this works. I will check it later." The cost of holding the hypothesis is roughly free; the human has institutional memory, intuition, a sense of what the code does that does not require running the code to confirm. They can defer verification without losing fidelity. An agent cannot. It has no intuition about your codebase. The only ground truth it has access to is what the tests say, what the type checker says, what the build says. When those signals are cheap, the agent uses them constantly. When they are expensive, the agent stops using them and starts speculating. Speculation by an agent looks plausible. It produces code that compiles, follows the patterns it has seen in your repository, names things sensibly. The problem is that plausible is not the same as correct. The agent that speculates is shipping a guess; the agent that iterates is shipping a tested answer. From the diff alone, they can be hard

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Everyone is adding AI to their product right now. Most of them are doing it wrong. Not because they chose the wrong model. Not because they used the wrong library. But because they treated AI integration like a regular feature and skipped all the engineering discipline that production systems require. I have integrated LLMs into multiple production applications. This is what I wish I had known before I started. The Mental Model Shift You Need First A traditional API call is deterministic. You send a request, you get a predictable response. You can write tests against it. You can cache it. You can reason about it. An LLM call is not deterministic. The same input can produce different outputs on different runs. The model can refuse, hallucinate, or return output in a format you did not expect. Your system needs to be designed around this reality, not in spite of it. This means defensive parsing, fallback logic, output validation, and graceful degradation are not optional extras. They are the core of the feature. Choosing the Right Model for the Right Job The biggest LLMs are not always the right choice. I learned this building EditDeck Pro, an AI creative platform for music. Some tasks needed a large frontier model for nuanced creative output. Others needed a fast, cheap model that could run many times per session without accumulating significant latency or cost. The pattern that works: Use a lighter model for classification, extraction, and short structured outputs. Use a larger model for generation tasks where quality matters more than speed. Route dynamically between them based on the task type. This can reduce your inference costs by 60 to 80 percent on workloads that mix simple and complex tasks. Prompt Engineering Is Software Engineering Prompts are code. They should be versioned, tested, and reviewed like code. I store prompts in a dedicated module with version numbers. When I change a prompt I run it against a fixed evaluation set of inputs and compare the out

The port seems solid, and all DLC is supported—but there's no crossplay, sadly.

I was looking at my bank statement one day and realised I was paying for 4 things I completely forgot about. Combined it was around 40 euros a month just silently leaving my account. I'm a 17 year old developer from Cyprus and I spent the last few weeks building Capsule, a simple subscription tracker that shows you everything you pay for, alerts you before renewals, and tracks how much you save by cancelling things. No bank connection required. You just add your subscriptions manually. Privacy first. It's not on the Play Store yet but the waitlist is live at capsule.crickdevs.com if anyone wants early access. Would genuinely love feedback from real people before I launch.

AI workflow automation looks simple in demos. A form submission comes in. An AI model reads it. The CRM gets updated. A Slack message goes out. An email is sent. But once you move from demo to production, the workflow becomes more sensitive. What happens if the AI summary is wrong? What happens if the CRM is updated with incomplete data? What happens if the customer request needs human approval before the next step? What happens when a workflow fails halfway? That is where AI workflow automation needs better architecture. In one recent project, we designed an AI workflow automation system using: Make.com for workflow orchestration FastAPI for custom backend logic OpenAI/GPT APIs for summarization and structured output Monday.com CRM for record management Slack for internal notifications Gmail for email-based communication Human review steps for approval and control The goal was not to build a chatbot. The goal was to reduce repetitive manual review work while keeping the workflow controlled, traceable, and practical for daily business use. The workflow problem The original workflow had several manual steps: A new request came in. Someone reviewed the request manually. Important information was extracted. A CRM record was created or updated. The internal team was notified. A follow-up email was prepared or sent. The team tracked the workflow manually. This kind of workflow is common in service businesses, operations teams, sales teams, and CRM-heavy processes. The pain was not that any one step was too difficult. The pain was that the same steps repeated again and again. That makes the workflow slow, inconsistent, and dependent on manual copy-paste work. Why not fully automate everything? The obvious idea is: Let AI read the request and update everything automatically. But that can be risky. AI-generated output can be incomplete, overconfident, or slightly wrong. That may be acceptable if the output is only a draft. It is not acceptable if the output directly updates

Every Linux PHP developer knows the dance. You need to switch from PHP 8.1 to 8.3. You run your sudo commands, update your global symlinks, and then realize your local development server in the other window just crashed because it was running on the old version. Why should managing PHP versions be a system-wide struggle? The Solution: Per-Shell Version Isolation phpvm brings the seamless developer experience of tools like pyenv , rbenv , or nvm to the PHP ecosystem on Linux. Instead of changing /usr/bin/php globally, it uses a lightweight shim directory prepended to your PATH . When you call php , the shim inspects your environment variables and forwards the execution to the correct binary. It supports three layers of resolution, falling back gracefully: Shell pin : Pinned manually via phpvm shell <version> Project default : Resolved from .php-version or composer.json requirements when you cd into a directory Global default : The system fallback managed by update-alternatives Effortless Provisioning No need to look up repository installation guides. The built-in installer automatically detects your distribution (Ubuntu or Debian) and configures the appropriate upstream repositories (Ond?ej Sur�'s PPA or deb.sury.org ) to fetch the exact CLI and FPM packages you need. Polish in v2.5.1 Our latest release focuses on making the environment rock-solid: Tray App Auto-Start : Spawns the GTK desktop tray app immediately after installation by resolving the graphical session environment from active processes. PATH Priority : Actively prevents IDEs, login shells, or snap profiles from overriding the shim's position in PATH . Clean Cleanup : Ensures all background processes are terminated during uninstallation. Getting Started You can install or upgrade using the interactive script: curl -fsSL https://raw.githubusercontent.com/rijverse/phpvm/main/install.sh | sudo bash If you are already running v2.5.0, simply run: phpvm --self-update Check out the project website, or find the

GPGPU.js: Run JavaScript on Your GPU With Zero Shader Knowledge Sven Herrmann Sven Herrmann Sven Herrmann Follow May 25 GPGPU.js: Run JavaScript on Your GPU With Zero Shader Knowledge # webgpu # javascript # typescript # performance Comments Add Comment 4 min read

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Large exchanges are designing derivative products around AI tokens, which are increasingly being considered less a computational output and more a raw material input, like electricity or bandwidth.

the video generators are becoming much more powerful, only unemployed people can track the changes ( like me).. Here are the current observations, and add anything in the comments if you feel I missed something. Cinematic Videos Seedance 2.0 : This Chinese model is fantastic in real visuals and advanced visuals, almost like real shots. I guess this will become the future. Kling 3.0 and kling motion transfer: Motion transfer is amazing, you shot a vidoe yourself and can trasfer the movement any avatar. Kling is the king in that aspect. With Kling’s motion transfer, . There is no other technology that can do this this well and look super fantastic. Veo3 : Recent releases of Veo 3.1 are still some of the best videos. Sora has shoted down by openai, and recent Google model, - GeminiOmni , is the best in video editing. It is like Nano Banana for videos. It is absolutely fantastic. Don’t compare this with Seedance because the purpose is completely different. If you try it on your own video and ask it to add something, it gives a super realistic output. Explainer Videos These are not cinematic, but mostly for concept explanations and long videos. These tools are great fit: Distilbook : This one is very good at creating visual explanations with whiteboards and animations based on your content, PDFs, and all. If you want long videos, like 3-minute or 5-minute training videos,academic this is purpose-fit. NotebookLM Video overview : This tool has the video overview option, which makes things much easier for you. It is mostly for slide-type videos, but it still gets your work done because most of the time you may not need animated videos. MathGPT: Here it is mostly for math educational video explanations using some animations. These are not very advanced, but still, if you want cheap educational videos, maybe it can do the job. Images In my personal opinion, - The recent GPT image model is fantastic. Second, the Google model Gemini Nano Banana Pro and Nano Banana Flash 2 are b

As Apple tries to shrink Gemini for the iPhone, a cloud component is probably inevitable.

I have spent the last few months working on infrastructure and tools to give agents global ids, and the ability to communicate. That is up and running now, but actually structuring their work together has been a real pain: I still have to give them roles and responsibilities, and start the agents in the right directories with the right id so that the actually get things done. I have automated that part now: a team can be bootstrapped from a template with one command: aw team bootstrap https://gi

For a long time, I thought productivity was about effort. Work harder. Focus more. Stay disciplined. Manage time better. Most productivity advice is built around some version of this idea. Then I noticed something strange. Some days I could spend ten hours at a desk and accomplish almost nothing. Other days I could spend three hours working and make more progress than I had all week. The difference wasn't effort. The difference was context. The Most Expensive Thing Is Not Time Ask people what their most limited resource is and most will answer: Time. But for knowledge workers, engineers, researchers, writers, and designers, I think the scarcer resource is often something else. Mental state. The ability to hold a problem in your head. The ability to remember why a decision was made. The ability to see connections between ideas. The ability to continue a train of thought without interruption. That's the state where meaningful work happens. And it's surprisingly fragile. Every Context Switch Has a Cost Imagine you're debugging a difficult issue. You've already: read the logs inspected the code traced the requests formed a hypothesis You're finally starting to see the shape of the problem. Then: a Slack notification arrives someone schedules a meeting an email requires attention a different task becomes urgent The interruption itself might only take two minutes. The real cost is what disappears. The mental model. The momentum. The partially constructed map inside your head. The next time you return to the task, you don't continue where you left off. You rebuild. Software Often Creates The Problem It Tries To Solve One thing that surprised me after building products for years is how much software exists primarily because other software creates friction. A note-taking application exists because memory is limited. A task manager exists because priorities change. A research assistant exists because information is fragmented. Many tools are not solving fundamental problems.