标签：#r

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

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

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

Originally published on hexisteme notes . A while back I wrote about why your vibe-coded app looks worse than you expect. That post diagnosed the cause. This one is the fix that actually worked, on a real job: redesigning the mascot in my trip expense-splitting app. The mascot is the face of the app. It shows up in more than twenty places — onboarding, settings, the stats screen, the map, the diary, the settlement report, and five little mini-games. And it was nothing. One circle did double duty as head and body. No legs. No hands. No eyebrows. One X for an eye. Visually its identity was zero: a tinted circle. I knew it was bad. What I could not do was say what to change. Words don't converge on a picture I kept talking myself in circles about it, and so did the AI I was pairing with. Rounder? Add a hat? Bigger eyes? Every sentence sounded reasonable and none of them moved the decision. At some point I noticed what was actually going on: this was not a shortage of information. Nobody needed to go fetch a fact. It was a shortage of fidelity . A visual decision cannot converge in prose, because prose is not the medium the decision lives in. That is the tell. When a discussion loops and more words don't help, you don't need more analysis — you need a picture. So I stopped arguing and built prototypes. Three variants, not more tints The rule I gave myself: make variants that are structurally different, not palette swaps. Different silhouette, different anatomy, a different device carrying the identity. Repainting the same shape in different colors teaches you nothing. Three genuinely different creatures force a real choice. I built three and rendered every one as an action sheet so I could look at them side by side: A, a jelly bean. The safe evolution of what I already had. It slots into the UI cleanly, but its whole identity hangs on a single coin floating over its head. Shrink it and it's just a round blob again. B, a wallet. Object personification: a wallet body with

Your people, your agents, your project — all in one place Discussion | Link

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.

Shashua has been invited to take the chairman of the board seat.