标签：#Product

Enable agents to keep context & work across apps + sessions Discussion | Link

The hidden cost of AI isn't generating code. It's understanding your codebase. For a long time, I assumed AI coding tools became expensive because they generated a lot of code. These tools can produce components, tests, SQL queries, documentation, and sometimes entire features on demand. If costs were climbing, the output volume must be the reason. The more I used these tools, the more I realized I was measuring the wrong thing. The expensive part isn't writing code. The expensive part is understanding what code should be written — and that work is mostly invisible. That realization changed how I think about AI-assisted development entirely. Two Prompts, Two Very Different Problems Consider these two requests: "Create a utility function that formats dates" and "Review this feature and suggest improvements." At first glance, both look ordinary. Both might even produce short answers. But they require completely different levels of understanding. The first is narrow and well-defined. The AI needs very little information before it can produce a useful answer. The second is open-ended. Before suggesting a single improvement, the AI may need to read multiple files, understand dependencies, follow existing patterns, compare implementations, and build a mental model of why the feature exists at all. The output might still be small. The work required to reach it is not. Why Agent Workflows Feel Different From Autocomplete This became much clearer when I started using AI agents. Traditional autocomplete is predictive — you type, the AI guesses what comes next. It's fast, cheap, and deliberately context-light. Agents behave differently. When you ask one to improve a feature or review a workflow, it doesn't immediately start generating code. It starts reading. It follows imports, finds related files, and tries to understand the system before touching it. That is exactly what makes agent workflows feel slower and more resource-intensive than autocomplete: they are spending effor

A shared workspace for AI agents and humans. Discussion | Link

I’m building Obex, a faith-rooted self-control app for men who want to quit porn and stay consistent with daily discipline. The stack is Expo / React Native, with a web landing page and a desktop blocker companion. Core features: Streak tracking and rank progression Panic Mode for urgent moments Accountability partners Blocker support on desktop Christian-focused language and reminders The goal is to make the product feel practical rather than preachy. People usually respond better to clear feedback loops, a visible streak, and a calm recovery path after setbacks. If you want to see it or give feedback, the site is here: https://obex.so

A context-efficient CLI coding agent built by agents Discussion | Link

Your Mac talks back when you plug things in Discussion | Link

GPT-Rosalind advances life sciences research with enhanced biological reasoning, medicinal chemistry expertise, genomics analysis, and experimental workflow capabilities.

A little hand on your Mac Discussion | Link

Collaborative voice API for agents Discussion | Link

Fight the scourge of unsolicited rings and pings from spammers, scammers, and telemarketers.

The company’s RTX Spark chips might finally turn the “AI PC” into reality.

How your front fork and rear shock work, so you can hurt less and have more fun.

Turn your iPhone into a free Mac controller Discussion | Link

Keep your trio of Apple gadgets powered up wherever you go with these compact folding chargers.

The AI world is full of old infrastructure with stochastic organs. That sentence probably explains...

A dynamic calendar scheduler with video backgrounds Discussion | Link

Multiplayer markdown for you, your team, and your agents. Discussion | Link

TL;DR Day 2 of AI Native DevCon shifted from agent capability to operating discipline. The strongest sessions focused on how teams can run AI-native delivery with clearer context pipelines, measurable agent behavior, safer execution boundaries, and better organizational ownership. The scale showed up in the numbers too. Across the two days, DevCon brought together 650+ in-person registrations, around 2,000 online registrations, and a packed mix of sessions, workshops, hallway conversations, and practical lessons. Day 2 leaned into workshops. That shift mattered because the second day was less about proving agents can do useful work and more about showing how teams can make that work repeatable. Hey there, welcome back. Rohan Sharma here again continuing the devcon series. Day 1 gave us the framing, including Guy Podjarny ’s core point that skills should be treated like real software assets. Day 2 picked up from there and moved into the operating details. Once agents are inside daily engineering work, platform and product teams need to decide what changes first, who owns those changes, and how the results are measured. Talks that shaped Day 2 Harness engineering beyond code Marc Sloan from Tessl focused on the next gap many teams are hitting. Code context is increasingly structured, but product and design context still lives in external systems such as Figma, Notion, and Linear. Pulling that context live can reduce staleness, but it introduces drift in evals, versioning, and reproducibility. The practical lesson was to stop treating external product and design context as random reference material. Teams need a defined layer between the repository and those external systems, with clear versioning so evaluations can be replayed against known context snapshots. Without that, agents can produce work that looks technically correct while missing the product constraint that actually mattered. That is a very expensive kind of almost-right. From vibes to metrics Simon Obstbau

I run multiple Claude Code sessions all day — one per feature, one per service, sometimes five at once. Every session was asking me for permission in its own terminal. I'd miss requests buried in a background tab. I'd switch windows mid-thought just to approve a git status . I'd lose context constantly. And there was no single place to see what Claude was doing across all of them. So I built Gatekeeper — a TUI daemon that intercepts every Claude Code tool call and routes it to one unified approval dashboard. The dashboard Three panes, one terminal: Left — all active Claude sessions, with status badges: [auto] means auto-approve is on, [linked] means it's wired to a terminal window Middle — pending permission requests with an age timer so you know what's been waiting longest Right — full request detail, danger warnings, and the numbered approval menu Every Claude Code tool call — Bash , Edit , Write , Agent — passes through a PreToolUse hook before executing. The hook connects to Gatekeeper's Unix socket, sends the request, and blocks. Gatekeeper shows it in the UI. When you decide, the answer travels back and Claude proceeds or stops. Approving requests The menu in the right pane mirrors Claude Code's own style: 1 Allow once 2 Always allow 3 Deny ↑ / ↓ moves the cursor, Enter confirms. Or just press 1 , 2 , 3 directly. A and D are quick shortcuts for allow/deny. Option 2 — always allow — is where it gets useful. Choosing it saves a persistent rule so the same request never surfaces again: Bash → saves the command pattern (e.g. npm run * ) to config Edit / Write → saves the directory to an allowlist Agent → enables auto-approve for that session The rule is written both to Gatekeeper's own config and to Claude Code's settings.json allowlist — so Claude Code itself won't prompt for it either. Auto-approve sessions Press A in the Sessions pane to mark a session as trusted. It shows [auto] — routine tool calls pass silently without appearing in the queue. But some things

2× Claude Code Pro's usage at $15/mo Discussion | Link