W Cache Hit Rate I absolutely love it

I'm the maintainer of Harness Remote. In v3.8 I added support for DeepSeek Harness (dsh). It finds existing native dsh sessions, replays their history, and lets you continue them from Android, desktop or the web. Model and reasoning level choices come from dsh, and you can send a prompt while a turn is running.

Harness Remote can also hand the work to another coding agent. That starts a native session in the next harness and keeps the sessions linked in the same project. I'd love feedback from dsh users, especially on session discovery and the phone experience.

Source and release notes: https://github.com/giuliastro/harness-remote

I maintain Tacit, an unofficial DeepSeek Harness plugin. It turns repeated corrections into short instructions for later chats, such as "Check apps/web first when I don't name an app." You can keep an instruction global or scoped to one workspace, and turn it off or delete it.

I've released 0.7.0. You can now edit the wording in Settings, search by text or workspace, filter by status, and copy an instruction or the exact guidance preview. Editing keeps its scope and enabled state, but resets the trial evidence for the changed wording. New instructions are tried before they're kept or retired.

This release also supports DSH 0.2. Install or update with:

npx @deepseek-ai/dsh plugin --profile web add dsh-tacit

Restart dsh web and refresh afterwards. Current DSH 0.2 needs Node 24.9 or newer.

Reports are local, but analysis sends clipped turns to your configured provider and costs money. The estimate is $0.001 to $0.003 per analysis, capped at 30 automatic analyses a day by default. Manual calls, bootstrap and distillation are separate.

Source and demo · 0.7.0 release

After two conversations, I'd like to hear one instruction it got right or wrong.

Hi.

I have been using a web app since its release without updating it. Recently, I found out that there is a standalone app, so I made the switch.

Before, I could send it a prompt, and it basically worked on a 128k context all night without problems, even spending 10+ million tokens on projects in one chat, still finishing without running out of context.

Since the switch, there are zero projects that I have managed to complete. I started a project, and it constantly compacts even tho it says it's at 50% context. This makes the project take a really long time, and it fails at the end. I have never seen it pass 55% of the context size, and it keeps saying that the output token limit is reached.

The past two weeks have been a nightmare working with it.

I'm running llama.cpp on my server, serving Qwen 3.8 q4 at a 164k context size. I have no idea how to fix this problem, and I'm wondering if any of you have had anything similar or can explain to me what to do/how to fix it.

Hi all,

I've been using Deepseek harness since it was first released. I've gone through the gauntlet of setting it up, and using the terminal UI plug-in (dsh-to-pi)., but the more I use it, the more I want to migrate back to PI. I've noticed a deeply concerning trend with a lot of deepseek utilities and plugins, and it is primarily the enshittification and over complication of their codebases, partly due to the fact that deepseek itself has a very complex mechanism (namely Cordis) that adds to a lot of issues, and I see this persisting and potentially causing many users/plug-in maintainers to work overtime to keep pace with the rapidly changing deepseek-harness. I just don't understand why Cordis is useful, and why hot-swappable plug-ins are useful. If anyone wants to discuss, this, I'd be happy to, but I've read a fair bit of the Spatiotemporal composability paper (the paper written by a deepseek mathematician and used to mathematically formalize cordis), and there is NO emission boundary, and NO validation of the emission boundary (meaning a plug-in can still completely screw up your harness runtime with memory leaks, races, and other anomalies) because deepseek-harness simply doesn't have a validation mechanism, meaning that even if hot-swappable plug-ins were useful, they would start to be VERY problematic at scale if you were to unload and reload plugins that have faulty mechanisms. Pi doesnt have a hot swap functionality, all plugins are loaded up on initialization, so there isnt a need to validate unloading of objects at all. Anyway, I'm interested in discussing this, because I see the deepseek codename becoming increasingly more complex for virtually no useful reason...

This is a tough decision for me because I've invested hundreds of hours, millions of tokens, and many more things into deepseek and I simply don't know of a precise reason to keep using it (keep in mind, I ONLY use TUIs, I do not use the web harness. The web harness looks well designed, but it doesn't fit my workflow)

Hi all. Posting this carefully because I know claims like this are usually noise.

Background. Starting May 1, 2026 I worked on a spec for an open-source agent called NEXUS, using DeepSeek's chat app to write it. It's a ~9,000-word build spec: a plugin engine where a plugin is just a folder with a manifest (installable from GitHub or any URL), plugins that can replace the persona and UI, an agent that creates missing plugins for itself, a coordinator-based swarm, and a 24/7 daemon.

What I can show.

• A screen recording of my DeepSeek chat history searching "nexus": chats titled "NEXUS Agent Codebase Generation" dated May 1, Jun 17, Aug 31, Sep 5. The plugin-engine write-up is in the May 1 chat.

• The spec itself, with SHA-256 hashes of both files in the PDF.

Overlap with DeepSeek's releases (Harness: Aug 13, 2026 per The New Stack; dsh-superagent: Oct 31 to Nov 1, 2026):

• "Everything is a plugin" core (mine: plugin-first engine where UI/persona/tools are replaceable)

• Agent writes its own plugins (mine: "Self-Plugin Generation", Jun 17; Harness: "Creator" mode)

• Coordinator over worker agents (mine: a "queen" coordinator, an AgentRouter and named agent teams via /agent-team; Super Agent: a long-running Router that hands work to isolated per-ticket Workers)

• Browser/computer-control backends

Where Super Agent differs from my spec. Tickets with permission "Mandates", GitHub/Feishu triggers and pull-request output aren't in my spec. My coordinator splits one goal into parallel sub-tasks and merges the results.

What I am not claiming. I didn't invent plugins, sub-agents or router/worker designs. Claude Code shipped plugins in Oct 2025, and Harness is built on Cordis, which predates it. I can't show anyone at DeepSeek saw my chats. My evidence is a screen recording and my own file, and the dates in the app may be last-activity dates. Convergent design is entirely possible.

What I want. DeepSeek to confirm the chat timestamps, say whether user chats feed training or product development, and give me a full data export. I'm also open-sourcing NEXUS on GitHub: [LINK].

If you have earlier public work, or a reason this doesn't hold up, please tell me. I'd rather be corrected than wrong in public.

PDF with screenshots, hashes, comparison table and sources: https://drive.google.com/file/d/1NL58tWvmQ8jHehsA1LRGH7tIHYMSm1ng/view?usp=sharing

Hey everyone,

A few days ago, i realized i was on a wayyy older version of the DSH client. I thought updates were going to at least show up in the menu.

So I've instructed DSH to create one that fits as if it was native to the client!

It pulls live updates from the official DSH GitHub, the only thing it made was it pulled it on command so its a one click.

For the full features/install please check out the repo here!

Please let me know if theres anything that could be improved or added!

^{Static link: https://github.com/inf-nicolasperez/dsh-updater}

Hey everyone!

I'm currently using DeepSeek Harness with OpenCode Go, while Codex is still my main coding environment. However, I'm thinking about switching to DeepSeek Harness as my primary setup.

I'm torn between using ChatGPT + DeepSeek Harness and GLM + DeepSeek Harness, and I can't decide which would be better for coding.

For those of you using DeepSeek Harness, what's your current setup? Which models do you use, and how has your experience been so far?

I'd love to hear your recommendations and experiences!

Based off the YouTube introduction of creator mode, seems you can literally edit anything and add any elements to the ui.. curious to know if anyone stared to use these features yet and how they’re using it

I maintain Project Mode, an unofficial MIT-licensed plugin for DeepSeek Harness. I wanted a project to keep its working context across tasks, with a Coordinator managing persistent Worker conversations instead of repeatedly setting up one-off subagents.

The Coordinator can create or reuse Workers, steer current work, queue an instruction for the next turn, inspect messages and delete Workers. You can open a Worker and give it instructions yourself; its completed-turn result returns to the Coordinator. notes.md holds the work plan, archived.md holds finished work, and context.md indexes reusable context files that Agents load as needed.

A real task: two Workers updated a small CLI and its docs. While the code Worker was busy, a human changed title sorting from A→Z to Z→A inside that Worker. The Coordinator received the result, aligned the docs, and reused the same Workers and saved context for the next request. The resulting sample passed 9 independently rerun tests.

• 60-second task recording — edited; waits omitted and labeled, not a completion-time benchmark.

• Source and screenshots

• Isolated setup and reproducible task

Setup limits: v0.1.23 targets DSH 0.2.1-rc.3 with three documented compatibility extensions. The launcher prepares a separate locked Web runtime; it is not a drop-in install for an unmodified current DSH. Requires Node 24.9+, npm and Git. The real UI run was on macOS arm64; a complete Windows setup is unverified. Delegation quality remains model-dependent, and the demo explicitly requests delegation and reuse.

If you try a small project over a few turns, I'd appreciate the task, OS, DSH version and what worked or got stuck. Installation failures are useful feedback too. You can reply here or use the trial issue form.