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a local, Claude-powered brain-assistant for solopreneurs I kept losing good ideas — not because I forgot to write them down, but because nothing ever went back and connected them. A task on Monday, an idea on Wednesday that was secretly the same problem, a question on Friday that contradicted something I'd decided two weeks earlier. All of it just... sat there. So I built Dot Connector : a small local app where every capture is a "dot," and every few captures, Claude reviews the stream against a standing memory and surfaces three things — non-obvious connections between notes, contradictions with what you said before, and open loops you haven't closed yet. The architecture is deliberately simple. It's Express + vanilla JS, no build step, no account system. Every capture gets a cheap, fast Claude call for auto-tagging. Every 3rd capture triggers a deeper "sweep" — a second Claude call that looks at recent captures alongside standing memory and open loops, and returns structured updates: new memory facts, dot-connects, contradictions, and open-loop resolutions. Why local-first. no server of my own, notes never leave your machine except the direct calls to Anthropic's API, bring-your-own API key so there's no subscription — you pay Anthropic directly, typically well under $1/month for personal use.] Its just a steal one-time $29 download All info and download get it here: https://dot-connector.eu

It’s not just you: AI startups are taking in a huge amount of seed funding, and in the process making things harder for anyone looking for funding even at a pre-seed stage. We’ve covered the trend in detail, and at this year’s TechCrunch Disrupt event, we want to help pre-seed founders now being held to seed-stage […]

Xbox is currently in a "reset" period that includes laying off around 3,200 employees over the next year, involving deep cuts at beloved studios like id Software and Obsidian Entertainment. Now, in an attempt to show that things are still running fine, the company has announced a slate of upcoming projects at Bethesda Game Studios […]

Ben O'Mahony discusses building custom AI-powered Language Server Protocols (LSPs) that go beyond standard rule-based checkers. He explains how to instrument AI agents natively with OpenTelemetry to track concrete user actions (accepting, dismissing, or regenerating code fixes) as implicit labels, creating a continuous data flywheel to distill frontier capabilities into cheaper, local SLMs. By Ben O'Mahony

Introduction A Continuation of Shadow SCADA Terminal Velocity begins where Shadow SCADA left off — at the edge where digital audits meet the physical world. In the previous article, we explored how hidden infrastructures reveal themselves through aerial recon, magnetic anomalies, and environmental signals. Now we move deeper: into the physics of sensing, the light‑based pathways of diodes and photodiodes, and the high‑spec tools that transform invisible signals into readable intelligence. Modern audits are no longer limited to dashboards and logs. They extend into light, magnetic fields, environmental distortions, and sensor‑level truth — domains that traditional processes never touch. Section 1 – Diodes and Photodiodes: The First Gate of Physical Signals In modern audits, everything starts at the physical layer — where electricity and light move before any software or dashboard exists. Two tiny components sit at that gate: diodes and photodiodes. They look similar, but they do very different jobs. What is a diode? · One‑way valve for electricity: A diode lets electric current pass in one direction only, like a one‑way street. · Why this matters for security: Diodes are used to make sure information can leave a system but cannot come back in through the same path (for example, in SCADA or critical networks). · Simple image: Think of a diode as a door that only opens outward. You can exit, but nobody can enter through that door. What is a photodiode? · Sensor for light: A photodiode doesn’t control current—it detects light and turns that light into an electrical signal. · Where it’s used: In cameras, light sensors, security systems, and tools that “listen” to the environment through light. · Simple image: Think of a photodiode as a tiny eye that sees light and tells the system, “Something is shining here.” The key difference (in one sentence) · Diode = controls flow. · Photodiode = senses light. Diodes are about blocking or allowing. Photodiodes are about seeing and

I run a pipeline that generates explainer articles. LLM in the middle, structured output, published in several languages. It had been running for a while and the articles looked good: clean layout, a chart, and near the top of each one a confident little box with a statistic. Something in the shape of "68% of people never change the default." A number, a source, an authoritative ring to it. Not one of those numbers had been researched. The pipeline had never looked up a single statistic in its life. It asked the model for a number and printed whatever came back. I did not find this through a clever eval. I found it while cleaning up something unrelated and actually reading the prompt. The field that forced a lie The output schema had a required field. statistic.text and statistic.source , described in the prompt as an "eye-catching stat" for the top of the article. Required. Every article had to have one. The prompt also, helpfully, told the model what to do when it did not have a real number. It said to round to a safe order of magnitude. And it said to strip the year off the source, so the article would look evergreen instead of dated. Read that back slowly. The instructions were: always produce a statistic, make up a plausible magnitude if you have to, and remove the one piece of metadata that would let anyone check it. That is not a prompt that occasionally allows a hallucination. That is a prompt that requires one, every single time the model does not happen to know a real figure. So it produced them, confidently, in every language, each wearing a real-sounding source: a named institute, an industry association, a government statistics office. None of it had been looked up when it was written. This was content people actually act on, which is exactly the category where being wrong is not a rounding error. There was a second engine doing the same thing in the chart code. The block that generated the data visualization asked the model for "actual statistics from

Hello, I'm Rijul. I'm building git-lrc, a micro AI code reviewer that runs on every commit. It's free...

How I went from toggling a single GPIO pin to deploying intelligent, low-power firmware on the edge — and the lessons that shaped me along the way. The first program I ever ran on a microcontroller did exactly one thing: it blinked an LED. No operating system. No framework. No safety net. Just my code, a register, and a clock ticking a few million times a second. When that LED finally blinked at the rate I intended — not too fast, not stuck on — I felt something I hadn't felt writing software before. On a bare-metal system, nothing happens unless you make it happen. There's no runtime quietly cleaning up after you. That mix of total control and total responsibility is what pulled me into embedded systems, and it's the same thread that eventually led me to running machine learning models on the edge. This is the story of that journey — from a single blinking pin to intelligent devices that sense, decide, and act on their own. The Bare-Metal Beginning Bare-metal firmware is where you learn what a computer actually is. When you write to a memory-mapped register to toggle a GPIO, or configure a UART peripheral one bit at a time, there's no abstraction hiding the hardware from you. You read the datasheet. You read the reference manual. You get the clock configuration wrong, and nothing works — no error message, just silence. Then you fix it, and suddenly bytes are streaming out of a pin at exactly the baud rate you configured. Most of my early growth happened writing low-level peripheral drivers — UART, SPI, I2C, GPIO, ADC — on ARM Cortex-M platforms. These are the unglamorous building blocks, but they teach you the discipline embedded work demands: Every byte and every milliwatt matters. On a resource-constrained MCU, you don't get to be careless with memory or power. Timing is a first-class citizen. An interrupt that fires 50 microseconds late can break the whole system. The hardware is always right. If your code and the oscilloscope disagree, the oscilloscope wins. Th

I sat the Claude Certified Architect exam expecting questions about model parameters, context limits, and API flags. I got something else. The exam barely tests trivia. It tests judgment: given a broken agent and four plausible fixes, which one actually addresses the root cause? The interesting part was how few ideas the whole thing rests on. The same handful of rules kept deciding the "right" answer, and they are the same rules that decide whether an agent holds up once real users touch it. Below are the twelve I kept running into, plus the four traps that look like solutions and are not. This is my own study material, derived from publicly available exam guidance. It reflects how I build, not an official Anthropic position. The twelve rules Enforce determinism in code, not in prompts If a rule has to fire every single time, it is not a job for a prompt. A prompt is a suggestion the model usually follows. "Usually" is not a guarantee. When you need a guarantee, put it in a hook, a gate, or an allowlist. Code enforces. Prose requests. Pick the cheapest fix that hits the root cause Before you build a subsystem, try the levers that cost minutes: a sharper tool description, an explicit acceptance criterion, a config change. Most "we need to build X" moments dissolve once you test the cheap fix first. Reach for the classifier only after the one-line change fails. Bad tool selection? Start with the descriptions When an agent keeps picking the wrong tool, the description is almost always the culprit, not the model. Tool descriptions are the primary signal the model uses to choose. Rewrite them to say exactly when to use the tool and when not to, before you go anywhere near few-shot examples. Over-engineering is almost always the wrong answer Narrowing scope and improving the prompt beat a new subsystem far more often than engineers expect. Every subsystem you add is one more thing to debug, monitor, and keep in sync. Complexity is a cost you pay forever, not once. A bigge

A $400 million chip-backed loan points to the next wave of AI infrastructure deals.

FCC chair has been gifted at least $63,000 worth of tickets by CBS or its parent company.

Amazon's upcoming God Of War show has hit a major snag - it's now on the hunt for a new Kratos, after an on-set injury put its current lead actor out of commission. Sons of Anarchy star Ryan Hurst was originally cast for the role in January, with Deadline reporting that four episodes of the […]

Ask ten AI developers what tools they use, and you'll probably get ten different answers. The AI ecosystem is evolving so quickly that it's easy to believe you need every new framework, model, and application to stay productive. I don't think that's true. Over the past year, I've experimented with dozens of AI tools while building products, writing technical content, managing prompt libraries, and developing AI workflows. Along the way, my stack has become surprisingly simple. It's not built around the "best" tools. It's built around the tools that work well together. Here's the AI stack I rely on in 2026 and, more importantly, why each tool has earned its place. 1. ChatGPT: My Primary Thinking Partner ChatGPT is where most of my work begins. Not because it can do everything, but because it helps me think faster. I use it for: Brainstorming ideas Structuring articles Reviewing technical concepts Exploring architectural trade-offs Refining prompts Research assistance I rarely expect the first response to be perfect. Instead, I treat it like collaborating with a knowledgeable teammate who accelerates my thinking. 2. Cursor: My AI-Powered Development Environment When it's time to write code, I move into Cursor. Its strength isn't just code generation. It's understanding the context of an entire project. Whether I'm building a FastAPI backend, integrating APIs, or refactoring an existing codebase, having AI directly inside the editor removes a huge amount of friction. The less I switch between applications, the more productive I become. In fact, one of the biggest lessons I've learned is that adding more AI tools doesn't automatically improve productivity. Sometimes it has the opposite effect. I explored this idea in The Hidden Cost of Using Too Many AI Tools , where I explain why a smaller, well-integrated stack often outperforms a collection of disconnected applications. 3. GitHub: The Source of Truth Every project eventually ends up in GitHub. Not just source code. I

A few months ago, a founder posted about the SaaS he'd just shipped — built entirely with an AI coding assistant, not a line of it typed by hand. He was proud of it, and he had every right to be. Within days of launch, someone found the API key sitting in plain sight in the client-side code. It got used to bypass the paywall, spam the backend, and write garbage into the database. The founder spent the next stretch rotating every key, moving secrets into environment variables, and locking down the API endpoints that should have been locked down before anyone ever saw the site. Nothing about that story is about the AI being bad at its job. The AI did exactly what it was asked: build a working product, fast. Nobody asked it to think about what happens when a stranger opens dev tools. In the replies, someone made a simple point: AI is a great research aid, but shipping a large application still means understanding the code — copying and pasting isn't programming. The founder didn't push back. He agreed: he'd learned it the hard way. The same story, over and over Swap the platform and the same shape of story repeats. Here's the WordPress version — three separate, ordinary launches, three separate silent failures. A site goes live and Google never finds it. Somewhere in Settings → Reading, "Discourage search engines from indexing this site" got left checked — a setting every staging environment needs and every production site must not have. Nobody notices until weeks later, when someone asks why the brand-new site isn't showing up in search at all. A debug log sits in a predictable place, readable by anyone. wp-content/debug.log collects whatever errors WordPress throws — database credentials, API keys, fragments of user data — in plain text, at a URL automated scanners check within hours of a new site going live. Turning debug mode off doesn't delete the file it already wrote. The admin username is still admin . It's the default nobody bothered to change, and it happens

This button passes every automated accessibility scan we've thrown at it: <button class= "btn-primary" type= "button" > Check availability </button> And it breaks every AI agent that tries to book a room through it. The markup is clean: a real <button> , a proper accessible name from its text content, an explicit type . Nothing to flag. The failure isn't in the button, it's in what happens after the click. And no static scanner ever clicks. What a scanner actually sees Static accessibility scanners evaluate the DOM at a point in time. Usually the initial render: HTML parsed, framework hydrated, nothing interacted with. They check that state against WCAG rules, missing alt text, contrast ratios, label associations, heading order. That's genuinely useful. It's also a photograph of a lobby, when the task happens in the hallways. Here's what never appears in the initial DOM of a typical booking flow: The date picker that mounts when the check-in field receives focus The error message injected after a failed form submit The room-selection modal that opens on "Check availability" The loading state between "Book now" and the confirmation A scanner reports zero issues on all of these, for the simple reason that at scan time, none of them exist. What an agent actually traverses An AI agent completing a booking doesn't evaluate a snapshot. It walks the flow: reads the accessibility tree, decides on an action, performs it, waits for the interface to respond, reads the tree again. Every state transition is a place where the tree can lie to it. Let's look at three patterns we keep finding in real audits. All three pass static scans. All three stop an agent. 1. The modal that exists on screen but not in the tree { isOpen && ( < div className = "modal-overlay" > < div className = "modal" > < h2 > Select your room </ h2 > < RoomList rooms = { available } /> </ div > </ div > )} Visually: a modal. In the accessibility tree: a div soup appended somewhere in the body, with no role="di

Users are creating accounts on your new Saas. Yay (and not just family and friends or bots). Yay! Now comes the next step from every marketing handbook: capturing newsletter subscriptions. This article builds on Add Sign Up to Rails 8’ Authentication . Add a simple checkbox to let users opt in to product updates during signup. Store their preference using Rails Vault and manage the subscription with Rails Courrier . First, add Rails Vault and Rails Courrier to your Gemfile: gem "rails_vault" gem "rails_courrier" Rails Vault adds simple and easy settings, preferences and so on to any ActiveRecord model (I recently pushed 1.0.0). Courrier is API-powered email delivery for Ruby apps with support for Mailgun, Postmark, Resend and more. Rails Courrier is the Rails “wrapper” for Courrier. These two gems work really nicely together for this feature. Run bundle install and generate the Rails Vault migration: rails generate rails_vault:install rails db:migrate It creates a new file app/models/user/subscriptions.rb : class User::Subscriptions < Vault vault_attribute :product_emails_subscribed_at , :datetime # Add more subscription types as needed: # vault_attribute :marketing_emails_subscribed_at, :datetime # vault_attribute :weekly_digest_subscribed_at, :datetime end And updates your User model to use this vault: # app/models/user.rb class User < ApplicationRecord + vault :subscriptions has_secure_password has_many :sessions , dependent: :destroy end This keeps subscription data organized without cluttering your User table. More subscription types can be added later without database migrations. Now the plumbing is done, add a checkbox to your signup form in app/views/signups/new.html.erb : <%= form . check_box :product_emails %> <%= form . label :product_emails , "Subscribe to product updates" %> Update the Signup model to accept this parameter: # app/models/signup.rb class Signup include ActiveModel :: Model include ActiveModel :: Attributes attribute :email_address , :stri

This is the next big thing! I've been working on Moonlight AI for almost 6 months now and today I have big news. Automated Applications . That's right, Moonlight AI will have automated applications for the 2.0 version! What is Moonlight AI? Moonlight AI at first, was a project that was aimed to compete with Upwork. That definitely didn't work out even at the development phase so I had to pivot to another project. A month after the conception of the original version, I met a potential co-founder for a new initiative. Moonlight has been repurposed to be a capabilities mapping engine based on worker experience. We used a resume parser and a local LLM to map the capabiities based on skills and job experience alongside Github integration to use as proof of work. The project ultimately didn't work out, the potential cofounder was MIA so my only recourse was to rewrite Moonlight AI to be another thing, which ended up being a glorified job board which is how it works now. The Development Process Moonlight AI was vibe-coded in a night. Modifications were done the same way with different LLM models. LLMs today are what I call an automated entry-level developer since juniors is the wrong term because juniors at least have from 1-3 years of experience in the professional landscape and are more than just coding monkeys, where as entry-levels are just that, developers with 0 years of experience. Today, Moonlight AI should be done the correct way: reading the code and writing it too. Why let an LLM to do my job as well as think for me? DO YOU WANT ME TO GET ALZHEIMER? BECAUSE THAT'S HOW YOU GET ALZHEIMER! Anyways, exercising the mind is very important, that's what make us humans. People today have an obsession with automating their lives completely and end up like the humans in WALL-E. End of rant. The Future (Conclusion) I don't know the future, but I envision Moonlight AI to be a tool to make unemployed people lives a little less unbearable. I know how frustrating is to use a jo

By default, whenever you request a machine learning model, the underlying architecture saves gigabytes of tensor data into a hidden directory located directly inside your home folder ( ~/.cache/huggingface ). Because standard bare metal and virtual cloud configurations typically isolate the root operating system on a smaller, highly optimized boot drive, pouring 140GB+ of raw weights into the home folder guarantees absolute storage exhaustion. Here is the engineering blueprint to fix it cleanly on Linux. The Cache Location Trajectory When attempting to solve this problem, avoid outdated tutorials recommending deprecated parameters like TRANSFORMERS_CACHE . Environment Route Support Status Architecture Impact HF_HOME Active Master Route Safely redirects all models, datasets, and core assets globally. TRANSFORMERS_CACHE Deprecated Warning Fails to capture datasets and will be removed in version 5.0. HUGGINGFACE_HUB_CACHE Deprecated Warning Legacy routing path that creates unnecessary diagnostic warnings. 🛑 The Symlink Security Risk Creating symbolic links (symlinks) to trick the OS into routing files elsewhere is a common anti-pattern. Mapping these links improperly or running your workflow with elevated rights introduces privilege escalation vulnerabilities, compromising container and host security. Step 1: The Permanent Environment Override To change your Hugging Face cache directory on Linux permanently, target an expansive secondary storage array instead by appending a direct master route into your user profile configuration: # Create a dedicated folder inside your secondary storage array sudo mkdir -p /mnt/massive_drive/ai_model_cache sudo chown -R $USER : $USER /mnt/massive_drive/ai_model_cache # Append the master environment variable to your bash profile echo 'export HF_HOME="/mnt/massive_drive/ai_model_cache"' >> ~/.bashrc source ~/.bashrc Step 2: The Python Import Order Mandate If you declare your custom storage location programmatically inside an application

QCon AI Boston 2026 focused on the operational challenges of deploying AI agents, emphasizing the need for robust production infrastructure. Key themes included improving context management, ensuring security through a "harness" around agents, and adopting a comprehensive engineering model for AI. By Tatiana Fesenko

Every morning, airline dispatchers, grid operators, and farmers around the world make decisions based on the same thing: a weather forecast. While these forecasts are something that most people glance at for two seconds, weather predictions influence major strategic decisions in many industries, with real money, livelihoods, and even actual lives at stake. Farmers use…