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Every founder who's launched a product knows the drill. You've built something you're proud of. You're ready to share it with the world. And then you discover you need to submit it to Product Hunt, BetaList, Peerlist, and about fifty other directories if you want any chance of getting noticed. So you open your first submission form. You copy your tagline from your notes app. You paste it. You upload your logo. You fill in your description. Then you open the next directory. And you do it all again. And again. And again. After my last launch, I calculated that I'd spent nearly twelve hours just on directory submissions. Twelve hours of copying the same tagline, pasting the same description, uploading the same screenshots. It was mind-numbing work that pulled me away from the things that actually mattered, like talking to users and iterating on feedback. I knew there had to be a better way. That's why I built AutoSubmit.to. It's a simple idea: save your launch information once, and let it autofill everywhere you need to submit. The problem with product launches isn't the strategy or the timing or even the product itself. It's the operational overhead. Most founders underestimate how tedious the submission process becomes when you're trying to maximize visibility. Each directory has slightly different fields. Some want a short description, others want a long one. Some ask for your Twitter handle, others want your LinkedIn. Some require specific image dimensions. This variability means you can't just copy and paste blindly. You need to adjust your content for each platform, which adds cognitive load to an already repetitive task. By the tenth submission, you're making mistakes. By the twentieth, you're wondering if any of this is even worth it. AutoSubmit.to approaches this problem with a two-part system that's designed to be both simple and powerful. First, you create a launch profile on the platform. This is your single source of truth. You enter your product name, tag

Every time your bot merges a two-hour-old release into main, you are trusting a stranger's freshly published tarball to be the same one everyone else is looking at. Sometimes that release is a real bugfix. Sometimes it is a maintainer who fat-fingered a token, or an attacker who did not, and either way your CI cheerfully rebases against it before anyone had a chance to notice. On 2026-07-14, GitHub added a pause. Not a big one. But a real one. The actual change Dependabot version updates now sit on their hands for three days after a package is published. According to the GitHub Changelog, a release has to have been available on its registry for at least that long before Dependabot will open a version-update pull request against your repository. The cooldown is on by default and requires no configuration. It applies across every ecosystem Dependabot supports on github.com, and GitHub Enterprise Server picks it up in GHES 3.23. Security updates are exempt. If a fix for a known vulnerability lands, Dependabot will still open the PR the moment it can, because a three-day delay on the patch defeats the entire point of shipping the patch. That single carve-out is the whole design. Why three days is doing so much work Three days is not enough time to audit a package. Nobody is pretending otherwise. What three days is enough for is someone else to notice. Most malicious releases that end up on a public registry get pulled quickly once security researchers, downstream maintainers, or the registry's own scanners spot the pattern. The typosquats, the hijacked accounts, the crypto miners buried in a postinstall script: they all rely on being pulled into build automation before the pattern is visible. Dependabot's old default was to be that automation. Its new default is to let the pattern show up first. You can read this change as GitHub quietly admitting that "always up to date" was the wrong marketing promise for a supply-chain tool. The knob, and what shifted about it Cooldo

I found a slow, insidious memory leak in a Node.js API gateway caused by lingering event listeners; I fixed it by scoping emitters per request, enforcing cleanup in finally blocks, and adding leak‑aware tests and runtime safeguards—memory usage flattened and OOM restarts stopped. The Incident The gateway handled TLS termination, auth, and request fan‑out for many microservices. Over weeks its resident set size climbed in a staircase pattern until Kubernetes began OOM‑killing pods under load. The failure was gradual —light traffic ran for days, peak traffic crashed in hours—so it escaped casual monitoring. Investigation Heap snapshots and allocation profiles showed growing counts of small objects —closures, request metadata, and event listeners—rather than one giant allocation. Tracing revealed an internal event bus where request‑scoped listeners were attached but not always removed: an early‑exit authentication path returned before the cleanup function ran, leaving listeners that held references to request state. The GC saw those objects as live and never reclaimed them. The Fix (technical details) 1. Scoped emitters per request. Replace global emitters for request‑local concerns with a short‑lived EventEmitter created at request start. When the request ends, the emitter goes out of scope and the whole closure graph becomes collectible. 2. Guaranteed teardown via try/finally . Wrap the entire request pipeline so cleanup runs on success, error, or early return; the finally detaches any remaining listeners, clears timers, and releases caches. 3. Leak‑aware CI tests and runtime metrics. A harness simulated thousands of requests across code paths, captured heap snapshots, and asserted bounded object counts. Production metrics tracked listener counts and emitted alerts when thresholds were exceeded. 4. Operational safeguards. Added backpressure on accept queues, a soft memory threshold that disabled nonessential tracing, and rollout halting on excessive crash loops. Thes

How a distributed, event-sourced issue tracker built with developer ergonomics in mind may have a role to play in the next generation of agentic workflows Vision drift Harness engineering has recently popularized the idea of containing architectural drift in agentic workflows. What might be missing in the discussion is a similar issue on a higher level - vision drift . By vision drift I mean that the implementation no longer drifts only from the architecture - it drifts from the original product intent. And it seems like the risk may be obscured by restricted tooling. As long as the project management tools only present a snapshot rather than a traceable story, there is an increased risk of undetected drift. Drift is detected via specification audits over time. However, while code history easily can be traversed via Git, issue tracking essentially lacks this capability. Issue trackers tend to be excellent at answering the question “what is going on right now?”, but fail at answering the question “how did our work in this area evolve last month?” or “what went on this time last year?”, or “how did we get from there to there?”. Workflow audits When I set off to build Epiq, this was not a concern on my radar. Agentic coding was something I had heard distant rumors of, and in fact I was just pursuing the ideal developer experience . This pursuit did however lead me down a path of unorthodox architecture, which in turn resulted in an issue tracker with some uncommon properties. One of these is the ability to inspect historical state by time-traveling, and replay sequences. I have not yet encountered another issue tracker with these capabilities. Initially I thought of it as a gimmick feature. Imagine the wow-factor of replaying the entire sprint in a retro, visualizing the past 2 weeks as a short movie. I thought it would help out with reflection of how much (or little) work had been accomplished. Not until I set out to do my own first fully agent-implemented feature did

Why Pipelock Is an Egress Agent Firewall, Not an Inbound WAF The question behind the word firewall Security teams hear "firewall" and picture something inbound. A firewall, WAF, or IPS sits in front of a service. Traffic comes from the outside world toward the protected app. The control inspects requests before they reach the app and blocks malicious payloads at the door. That is outside-in protection. It fits web applications, where many attacks have recognizable request shapes: SQL injection, cross-site scripting, known exploit signatures, or malformed protocol behavior. The web server is the thing being attacked, and the attacker sends requests into it. AI agents invert that model. The agent is not only a server receiving input. It reads external content, calls tools, sends HTTP requests, invokes MCP servers, and runs with credentials. The dangerous event is rarely that a hostile packet reached the agent. The dangerous event is that the agent got talked into doing something with outbound effects. That is why Pipelock is built as an egress agent firewall, not a WAF-style inbound firewall. Why inbound filtering is the wrong primary model Prompt injection does not behave like a structured malware packet. It is natural-language instruction sitting in places the agent is supposed to read: a web page, a ticket, a search result, a tool response, an MCP server reply, or a user message. The channel is legitimate. The syntax is often normal. The attack is semantic and context-dependent. Solving that by filtering every input before it reaches the agent turns into an enumeration problem. You write patterns for "ignore previous instructions," then the attacker rephrases. You block one formatting trick, then the instruction is split across paragraphs, hidden in quoted text, encoded, or dressed up as policy text. Known phrases are worth catching, and Pipelock catches known injection markers in content it mediates, but input filtering cannot be the center of the security model.

"Increasingly in our world, it gets harder and harder to know what is real," Lorde said onstage.

The device is weirdly described as involving "mechanical elements that can move on their own" and the Bloomberg report includes the detail that the device is designed to "feel like a companion and become a physical manifestation of OpenAI’s ChatGPT."

Old and forgotten "shims" Microsoft failed to revoke have made Secure Boot bypasses simple.

OpenAI has issued another statement on the lawsuit, this time suggesting it lacks merit.

Hello all: I am just about finished with my first production-quality app. I developed it for macOS and Windows with full platform parity and am about to port to Ubuntu/Debian before releasing. I have licensing setup through Keygen.sh. I am happy with that decision so far; however, I have questions regarding that issue and the issue of telemetry. Privacy is a focus of mine and a commitment I made to prospective subscribers. That said, I need some form of telemetry and a DRM that can take subscription payments. I am trying to think of the best way to implement the Keygen.sh API for privacy. The app has, well, an app--the client--and it also has a remote server component. It will mostly be free but the more labor intensive developed elements and the tools that require the remote server will be paid at either a monthly or annual subscription rate. I am so confused about how to best implement the licensing API while remaining true to my privacy commitments. Part of me says, "Well, their information is going to be on Keygen's servers anyway, might as well host the store through their website/portal." The other inclination is to host the store on my server/website, so I can control how much information PII is required. The answer can be “very little,” but a much bigger question arises surrounding what to do with the information once accepted. (I’ve considered maintaining the subscriber data in some kind of encrypted space on the server with hardcoded, volatile keys. That way not even I can see their data, but they’d still be able to search for and regenerate their licenses, management payment methods, etc.) The other question I have is along that same vein and involves telemetry. What I would like to do is keep the telemetry deliverables locally stored. When the user has an issue, experiences a bug, or has some other issue requiring my troubleshooting assistance, I can request it and they can choose to release their telemetry data to me. In between those disclosures, using