Auto-curated dispatches on AI, React, Next.js, and the web — distilled down to what you actually need to read.
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Joshua Achiam spent nearly nine years at OpenAI researching AI safety and made a memorable appearance in the Musk v. Altman trial.
I rebuilt aesencryption.net so text AES (128/192/256) runs fully in the browser - the key and plaintext never leave the page. The hard part is staying byte-compatible with common server-side AES libraries (mode, IV, padding, base64 output), so I ship copy-paste equivalents in PHP, Java, Python, Go, Rust, Kotlin and JS. Live tool (mine, free): https://aesencryption.net - feedback on the crypto choices welcome. My own site.
Most "image to base64" sites upload your image to a server to convert it. For a format whose No upload, no canvas for the common case — FileReader hands you a data URI directly: const reader = new FileReader(); reader.onload = () => { const dataUri = reader.result; // "data:image/png;base64,iVBORw0K..." const base64 = dataUri.split(",")[1]; }; reader.readAsDataURL(file); The MIME type is baked into the data URI, so from one result you can emit raw base64, a full , or a CSS background-image. at i
I had to generate a few invoices lately and I checked some free tools, they were either asking me to signin or the format wasn't good enough. So, I vibe coded an invoice generator tool, which is completely free, requires no authentication and handles all the data in your browser. GenerateInvoice.online Do try it out and let me know your feedback.


un Trivy against almost any vendor container image and you'll get a wall of knowing that has VEX (Vulnerability Exploitability eXchange) turns that triage into Everything here is in a one-script demo repo: github.com/darkedges/trivy-vex-demo ⚠️ The demo marks every CVE not_affected mechanically to exercise the plumbing. In real life the assessment is the valuable part, don't ship Target: pingidentity/pingaccess:8.3.4-edge (digest sha256:51689e8c…). Tools, pinned and current at time of writing: T

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Large Language Models (LLMs) are powerful, but a single prompt rarely produces production-ready results. This is where Loop Engineering comes in. Instead of treating AI as a one-shot system, Loop Engineering treats it as an iterative process where the model continuously evaluates, improves, and validates its own output until predefined quality criteria are met. Loop Engineering is an AI development pattern in which an LLM operates inside a feedback loop rather than responding only once. A typica
I've been building security tools for AI agents and I have a genuine question for this community. When you ship an AI powered feature a chatbot, an agent, anything that takes user input and passes it to an LLM do you test it for adversarial inputs? I'm talking about stuff like Can a user extract the system prompt? Can they make the agent ignore its instructions? Can they get the agent to call tools or access data it shouldn't? I'm not asking to judge. I'm asking because I've talked to maybe 200
Originally published on Progressino By the Strategy Desk at Progressino Editor's note: This article explores AI Giants Are Handing Out Tons of Free Computing Power to Grab Startup Share - WSJ for founders, operators, and technology leaders planning their next investment cycle. Teams are under pressure to ship faster without increasing risk. AI Giants Are Handing Out Tons of Free Computing Power to Grab Startup Share - WSJ sits at the intersection of operations, customer experience, and technolog
Originally published at norvik.tech An in-depth analysis of the recent $1M payment by a US government body in a data theft case, exploring technical implications. A recent incident has unveiled a troubling reality: a US government entity reportedly paid $1 million to Kairos to suppress stolen files. This incident underscores the growing threat of data theft extortion, particularly when sensitive information is compromised without adequate encryption. The implications are significant for cybersec
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