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TechCrunch Startup Battlefield is coming to Australia — and we're partnering with Stripe to find the country's most exciting early-stage startups.

How a face-customization puzzle at HackMIT went from clicking sliders by hand to reverse-engineering a hidden formula from 10,000 API calls. Face Value looked simple at first glance: ten sliders (Face, Skin, Hair, Brows, Eyes, Nose, Mouth, Glasses, Mole, Accessory), each 0-9, controlling a cartoon avatar. A hidden model scored every configuration, and the goal was to find one it would fully accept : Confidence ≥ 99.9% Edit distance from the starter config ≤ 5 (only half the sliders could move) Charm check: pass Sync check: pass The puzzle's own hint: "Not all features affect the model equally. Some are more sensitive than others, especially together. Single-feature sweeps can be misleading." That warning turned out to be the whole game. Phase 1: Brute Force by Hand The first instinct is the obvious one: click a slider, hit Query, read the result, adjust, repeat. Every query returned four numbers, shown together in a Reviewer panel: Probability, Charm, edit Distance, and Sync. All four had to align at once. This works, sort of. Over the first ~24 manual queries, real patterns emerged: certain Glasses values seemed to matter for Sync, Mole and Accessory nudged confidence up, some sliders had sharp peaks rather than smooth slopes. But progress plateaued hard around 60-77% confidence . Manual testing can only really explore one or two dimensions at a time, and the puzzle explicitly warned that the model cared about combinations ; you can't discover a 3-way interaction by changing one slider and squinting at the result. The first real breakthrough was small but important: after enough fiddling, one query came back with Sync: True for the first time, confidence still low (8.14%), but proof that the four conditions weren't mutually exclusive. Phase 2: Escaping the UI The turning point was popping open Chrome DevTools, clicking Query once, and grabbing the actual network request as a curl command. Underneath the slick UI was a plain JSON API: POST https://facevalue.hackmit.

My project's docs had rules. "One document, one responsibility." "Split anything over 45 lines." I wrote that. The day before yesterday. Here's how that was going. Folders with no README (no index): 25 out of 37 The folder holding our engineering rules: 11 files, zero index Documents breaking the 45-line rule: 47 Largest offender: 1,203 lines Writing a rule down does not make it a rule. Obvious, I know. But there is something about being handed a list of 47 documents where you personally broke your own rule that stops being funny halfway down. (The me of two days ago fully intended to follow it.) I rewrote the rules themselves, and the rules file hit 150 lines The plan was already clear: let a machine enforce this. Fail CI. Which meant writing the rules properly first. README required, folder layout, update obligations, what CI actually checks. By the time it was all in there, the rules file was over 150 lines. The rules file was breaking the 45-line rule. Now, if you say "well, the rules file is special, it gets an exemption" — what have you just done? You have created the precedent "the rules are exempt," and it is permanent from that day. From then on, every time someone crosses 45 lines, they get to say "the rules file does it too." And they're right. So I split it. Eight files, all under 45 lines. If I can't follow my own rule, the rule was never worth writing. The moment CI landed, 63 existing violations bared their teeth On to the real work: write the checker, wire it into CI. Run it, and of course: 63 violations Everything fails. All red. Files I'm about to touch and files nobody has opened in months, equally red. Humanity is offered two choices here. Fix all 63 first, then turn on CI (including the 1,203-line monster) Add an ignore list, silence the 63, move on Tempting, isn't it? Option 2. It was to me. A .lintignore with 63 lines in it and a comment saying "remove later." So when exactly are you removing that list? Think about what that file actually is.

We've been testing a new project this week. The project's origin story is a mess. Upper management...

OhNine is a free menu bar app that tracks Claude session and weekly usage limits in real time It sends native alerts at 80%, 91%, and 100% so a session never ends without warning The hard problem was never reading a number, it was making the warning arrive before the cutoff instead of after Building a zero telemetry tool changed how I judge every product I ship after it The Problem: Hitting a Wall You Cannot See For months, my Claude sessions ended the same frustrating way. I would be deep in a conversation, mid thought, actually making progress, and then the reply would just stop. No countdown. No yellow light. No warning that said "you have three messages left, wrap up." One second I was working, the next I was staring at a message telling me to wait for a reset I never saw coming. The frustrating part was not the limit itself. Usage limits exist for a reason, and I understand why they are there. The frustrating part was the total lack of visibility into where I stood. Claude Code and claude.ai will occasionally mention you are close to a cap, sometimes at 97 percent, which is technically a warning and practically useless, because by then you are already mid-thought with no time left to land it cleanly. It got worse once I noticed the layers. There is not one limit to track, there are several stacked on top of each other: a session limit, a rolling weekly cap, and separate caps depending on which model you are running. Switching models mid-session, thinking you had found a workaround, only to hit a wall from a different direction, was its own specific kind of frustrating. None of these layers showed up anywhere. There was no dashboard, no menu bar icon, nothing you could glance at the way you glance at your laptop's battery percentage before deciding whether to plug in. So the wall kept arriving the same way: mid-flow, mid-sentence, with zero warning. Coding sessions got cut off between a question and its answer. Writing sessions lost momentum at the worst possibl

Welcome to the v0.0.2 release of Knowledge-and-Memory-Management, a tool designed for ingesting and managing knowledge from diverse sources. This release marks a clean release, stripping all hardcoded personal paths and replacing them with the portable $AGENT_HOME environment variable. For experienced developers, this version brings consistency and ease of deployment across environments without sacrificing the core functionality of knowledge collection and memory management. The project focuses on three primary collection pipelines: web, video, and articles. Each pipeline is modular, allowing you to configure, extend, or replace components based on your stack. The memory management layer ensures that collected data is indexed, stored, and retrievable via semantic search, making it practical for building personal knowledge bases or feeding into larger systems like agents or RAG pipelines. Knowledge Collection The collection module is source-agnostic at its core but ships with specialized handlers for common content types. Web collection uses a configurable web scraper that supports depth limits, domain filtering, and content extraction via readability algorithms. You can target specific sections, strip ads, and normalize HTML into markdown. The scraper respects robots.txt and supports session management for authenticated sites. Video collection transcribes audio using a local or remote ASR model. The pipeline extracts audio tracks, splits them into chunks, and generates timestamped transcripts. This is particularly useful for processing lectures, talks, or screencasts. The transcript is treated as a text document for further processing. Article collection handles RSS/Atom feeds and direct URLs. It parses feeds, fetches full content using readability engines, and deduplicates entries. Articles are converted into a consistent schema: title, author, published date, body text, and metadata. All collected data passes through a normalizer that converts content into a stand

Outside experts—some with a vested interest in peptides—recommended adding a number of the amino acids to the FDA's bulk list, including the “Wolverine stack” touted by Joe Rogan and other influencers.

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I'm a long-time macOS user and I just want a change. Curious what people here actually use every day and what made you stick with it. Curious if you also switched from a Mac too. I'd like to get out of the "walled garden" ecosystem.

The breathalyzer identifies the amount of acetone in your breath to determine if your body is in ketosis.