标签：#ha

We spoke with several cybersecurity researchers, who look for unknown vulnerabilities and develop tools to exploit them, about how OpenAI’s and Anthropic’s guardrails affect their work.

I would say this is the real factory simulator game haha

Hi, I developed Life Tile for iPhone. It tracks your time passively and visualizes it as a Mondrian-style grid. It's a one-time purchase app with a 7-day trial. I was a user of a similar app named Life Cycle. Since the app hasn't been updated in a while, I tried to make a better app with my ideas and experiences. I used a big square timeline instead of a donut chart. And I added more useful views: tree-map, map, and list. You can use categorized activities with custom colors. The data never leav

Hey everyone, my name is AbdulRahman Elzahaby ( @egyjs ), a software engineer from Egypt who’s recently fallen down the rabbit hole of LLMs. Like so many of us, you’ll find me in the thick of automation, bots, and making AI work for fast-tracking features and workflows. As I started diving into complex, multi-agent workflow automation, I experimented with… everything. I fiddled with n8n, played with OpenClaw, tinkered with Hermes Agent, andspent what felt like ages manually chaining together Python scripts with LangChain and LangGraph. Every tool showed promise, but my work became increasingly complex and every single workflow somehow felt... Unfinished. The way the industry seems to approach building these kinds of agents revealed a massive, structural gap in tool design. On one hand, we have intuitive, visual workflow tools like n8n. The drag-and-drop interface is great for a bird’s eye view of higher-level logic. But when you need more advanced concepts, like complex looping with conditional logic, custom state reduction, or a system that integrates properly with code review and versioning, these low-code boxes quickly hit limitations. On the other hand, we have powerful code-first frameworks like LangGraph. They provide incredible flexibility and raw execution power, but as soon as you start to build even a simple three-agent triage workflow, the boilerplate code starts to pile up. You have to write custom state schemas (TypedDict), initialize every node function individually, define custom logic for how to route between agents, set up the graph checkpointer, and grapple with environment and dependency management. What seemed missing was a sweet spot - a seamless bridge between the design intuition of visual workflows and the production-ready execution power of code-based frameworks. I wanted a tool that allowed me to simply design a workflow, and then run it efficiently without getting tangled in repetitive boilerplate code. Ultimately, I concluded that what we

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.