Something we noticed with publishers, content teams, and marketers - there is a very common blind spot in how digital documents get measured. Nobody really knows which pages held attention, which sections got skipped, or where most readers actually stopped. The production effort was real. The feedback loop was not.
We spent a lot of time sitting with that problem. The question was not "how do we add analytics" - it was "what would actually help an editor or content lead make a better decision before the next edition?" What we landed on: page-level data has to be specific enough to act on. Knowing that your document had 400 views is not actionable. Knowing that 60% of readers dropped off at page 4, and that page 4 happens to be the densest text page in the document - that is something you can work with. That thinking shaped how we built this into ZenFlip. The goal was not a dashboard full of numbers. It was a clear answer to the question: what should we do differently next time?
I've been working on a voice synthesis app that runs entirely on your Mac's Apple Silicon chip. It uses Qwen3-TTS (Apache-2.0) through MLX, so everything stays local no cloud processing, no per-character billing, no API keys.
Just shipped an update that bundles the model directly in the download, so first launch is instant instead of waiting for a 1.9GB model download.
I built squashImage for myself mostly and at that point it was just code. I only recently made it public and I desperately need insight because I know what I needed and that's all included, but what does the average user need and what would pull a user to use my tool. Thanks for any advice or support, all are appreciated. Check out my site through my published project on here.
That is the observation I have been circling for months: No language model reliably corrects itself not even when asked politely. It requires an external entity that is structurally independent.
This question has evolved into a tool that I have now released.
Conclave Personal brings multiple models into a shared conversation and assigns them explicit roles: the Writer drafts, the Critic challenges, and the Judge evaluates. Every execution is traceable as a "run" tracking status, errors, duration, and token usage. Conversations, agents, and the workspace remain local, and Ollama runs without an API key.
I built iisacc around a simple promise: practical software that raises human creativity and helps imagination become useful work.
Instead of making the site only a portfolio or a store, I connected four layers: the products, the human problems they are intended to solve, technical material, and the development decisions behind them. The current public work includes Vincent, local digital paper for visual thinking; WeUs, an AI-native social network in early access; LVRS documentation; and a free 77-page English eBook about shipping maintainable AI-assisted software.
I also expose unfinished commercial status rather than hiding it: Vincent sales are paused, WeUs is early access, and the advertising workspace is planning-only.
I use Codex Desktop and Claude Desktop side by side, and I often want a second agent to independently review work completed by the first one.
The workflow I m exploring is:
Finish a design or implementation in Codex Desktop.
Ask Claude Desktop to open a new, visible review session in the same repository.
Give Claude the relevant context from the source conversation not just the code diff.
Return Claude s findings to the original Codex task automatically.
To be precise about the existing landscape: Claude Desktop s Code tab runs Claude Code and supports plugins. OpenAI s codex-plugin-cc already covers much of the Claude Desktop / Claude Code Codex direction.
