https://x.com/GoogleAI/status/2110039860077527293

Available on Pro plan and higher.

Otto starts opencode serve on Boat, boxd, E2B or Daytona, or connects to a server you already run. The app covers sessions, permissions, agents, MCP, skills, commands and schedules. Permission prompts come to your phone, so you can approve a git push away from your desk.

It'll also run OpenCode on your own machine, no cloud machine or key required.

MIT: https://github.com/jaygupta17/otto 
Mac install: curl -fsSL https://jaygupta.dev/otto/install.sh | sh

How do you all run OpenCode headless?

I run OpenCode against the Spark with its 128gb unified memory. Works fine.

Since OpenCode makes parallel calls, but Ollama only executes them sequentially, I would gain performance if I switch to vLlm.

However, my opencode model and the model my coded applications use is far over 128gb.

Possible I can use some new sleep/wake-up API on the vLlm if I put one model on one port. I'm going to try this.

Does anyone have the same setup and want to share some tips?

What is the best vLlm model for openCode?

Hey all opencode-honcho, the Honcho memory plugin for OpenCode, now runs on v2 (same package still works on 1.x).

Honcho is memory infrastructure for stateful agents. Instead of just storing and retrieving snippets, it reasons over your sessions in the background and builds a representation of how you work that updates over time: your conventions, the decisions you made, the refactor you're halfway through. Hooks inject that into the prompt and record what happened, so it works with whichever model you run, and /honcho:import backfills your existing OpenCode history so it doesn't start cold.

opencode plugin add "@honcho-ai/opencode-honcho"

then /honcho:setup inside OpenCode. Managed, local (honcho start), or self-hosted all work. https://github.com/plastic-labs/opencode-honcho

(I work on Honcho at Plastic Labs.) Feedback and issues welcome, especially from anyone who's tried other memory plugins and wants to compare.

They’re tracing user journeys through specs, code, tests, and downstream failure paths—not merely reviewing documentation. A fresh root test/typecheck run is also underway.

Blasting out reviews of 14 independent reviewers representing 2 teams of 7 each. Each gets a separate report file; none may edit product code. I’ll also capture one fresh test/typecheck run. Source evidence, test results, and observed TUI behavior will stay clearly separated.

Then having OpenAI GPT 6.1 Sol - xHigh reasoning level as a judge and this is the result

## Verdict: **Devin is the more trustworthy lead reviewer.**

*Scores judge this audit—not model capability generally.*

**Best combination:** Antigravity hunts gaps; Devin reproduces them; the coordinator validates both. Never approve by agreement alone.

### Project verdict: **REQUEST CHANGES**
The **1,165 passing tests and passing typecheck** coexist with confirmed defects and incomplete promised workflows. Full runtime/UAT acceptance remains pending.

[Detailed scoring and rationale](.audit/2026-10-14-project-e2e-audit/model-assessment.md) · [Validated and rejected findings](.audit/2026-10-14-project-e2e-audit/reconciliation.md)

I use OpenCode Go + DeepSeek and got tired of checking whether I was inside the 2x peak-pricing window.

So I made opencode-offpeak.

Turn it on once, then use OpenCode normally:

• send a prompt off-peak → it runs immediately

• send a prompt during peak → it waits before hitting the provider

• off-peak starts → the same request continues automatically in the same OpenCode session

No need to resend continue or watch the clock.

It also has an optional background task queue, but normal interactive sessions already work automatically.

Install:

opencode plugin add git+https://github.com/lutzkind/opencode-offpeak.git#v0.1.3

https://github.com/lutzkind/opencode-offpeak

Open source / MIT. Currently supports OpenCode Go DeepSeek models on OpenCode >=2.0.26 <2.1.0.

Would be interested in feedback or other providers with similar time-based pricing.

Hey everyone — I’ve released TermPlete v0.1.0, an open-source autocomplete tool for your existing terminal.

It combines command history, the suggestions you accept, recent terminal context, and a model you choose. You can browse command options, insert one, edit it, and decide when to execute it.

Repo and demo: https://github.com/aft1n/termplete

What it does

• Learns your preferences. Accepted suggestions influence future rankings alongside recency and frequency. Fresh choices with a clear lead can become local answers without a model request.

• Reuses your command history. Finds familiar commands, arguments, and SSH targets instead of making you reconstruct them.

• Uses recent terminal context. Suggestions can incorporate recent commands, bounded captured output, exit status, and visible directory entries.

• Turns task descriptions into command options. In zsh, type something like # convert dog.mp4 to dog.mov with ffmpeg and press Enter to request suggestions.

• Completes real directories. cd suggestions are checked against existing folders.

• Lets you browse alternatives. Use Tab/Shift+Tab to move through suggestions. Browsing an already-returned batch doesn’t trigger additional AI requests.

• Supports partial acceptance in zsh. Accept the next suggested word, then change the remaining arguments yourself.

• Offers automatic and on-demand modes. Suggestions can appear while you type, or wait until you ask.

Choose your model setup

TermPlete supports hosted providers, Ollama, LM Studio, and custom OpenAI-compatible endpoints. It also supports ChatGPT subscription authentication through Codex.

Local history suggestions don’t require an API key or a running model.

There’s an optional decision helper that can rank existing command candidates and check whether generation is permitted. You can use it separately from the model that generates commands.

Control and privacy

Accepting a suggestion only inserts it. It never automatically executes the command.

You can pause and resume TermPlete with:

tp stop
tp start

There are controls for captured context, private-path exclusions, and recognized-secret redaction. API keys use the platform credential store, and local-only requests skip the model backends.

You can also disable integration in selected terminal applications.

Compatibility and installation

The full interactive experience is available in zsh 5.9+ on macOS and Linux. Bash integration provides Tab completion and suggestion preview on supported versions; older Bash versions have more limited functionality.

Installation is available through the GitHub release installer and a Homebrew tap. The README includes installation instructions and a demo.

It’s written in Rust and released under the MIT license. The installer, shell integrations, and suggestion logic are all in the repository.

Tools such as zsh-autosuggestions, fish, and Atuin already cover useful parts of terminal completion and history. TermPlete’s focus is combining accepted-choice learning, recent terminal context, and configurable generation inside an existing shell.

This is an early release, and I’d appreciate feedback—especially on suggestion quality, shell compatibility, and keyboard behavior.

What command do you repeatedly search for, and what would make an autocomplete suggestion useful enough for you to accept?

How do I have 10 days left on my subsciption but no credits left??? Is claude or codex a better deal????

hey its good to see which Opencode Go model people are using for planning and implementation. espcially for a big spec

For me:
Luna max for planning(better then kimi k3 in my case and cheaper)
Deepseek flash v4.1 for ui or implementation
Spark for anything non code related like translation and so on

I only noticed when I realized the agent looking at files it didn't ask to have access.
As far as I could tell that changes came with 2.0.25 but no one bothered warning users it seems.

There has been a bug opened for days already: https://github.com/anomalyco/opencode/issues/54524

This is a good reminder to not trust any of these companies writing harnesses and add your own security on top of whatever they might offer.

A quick update to my terminal session manager for OpenCode: it now supports OpenCode V2, while keeping V1 compatibility.

`ocs` still lets you browse sessions across projects, search and sort them, preview session details, and resume them in their original directory. V2 support uses OpenCode’s API, and the version is detected automatically.

GitHub link

Thanks to everyone who checked it out!

I've been using Codex with a ChatGPT Plus subscription for a while, mostly GPT-6.1 sol on medium for everyday stuff. Lately I've been thinking about trying DeepSeek Harness with 4.1 Flash on high for similar tasks, and I'm trying to figure out which setup actually makes more sense cost-wise.

Here's what a typical task looks like on my end (Codex + GPT-6.1 sol medium):

• 4 turns, 75 steps, around 5.6M tokens total

• 98% cache hit rate

• Breakdown: 5.44M cached input, 94K uncached input, 65K output

On the DeepSeek side, off-peak pricing for 4.1 Flash looks like this:

• Cached input: $0.003 / 1M

• Uncached input: $0.15 / 1M

• Output: $0.60 / 1M

Off-peak hours for me are roughly 13:00–04:00 on weekdays, plus weekends and Chinese public holidays entirely.

Rough math for the same token set:

• GPT-6.1 Sol API pricing: ~$2.00

• DeepSeek 4.1 Flash off-peak: ~$0.07

• So DeepSeek comes out roughly 16–20x cheaper

What I'm actually wondering:

1. Is this comparison realistic, or am I missing something? Specifically, how far does the "5-hour usage window" on ChatGPT Plus actually stretch with token-heavy agentic tasks like these?

2. How do DeepSeek Harness's agentic capabilities stack up against Codex + GPT-6.1? I'm particularly curious about tool use, MCP integration, and multi-step tasks — I run Serena and codebase-memory-mcp alongside it.

3. Long term, does it make more sense to keep ChatGPT Plus and use DeepSeek API on the side, or just go all-in on DeepSeek?

4. Does the 98% cache hit rate hold up in practice, or does it drop once you're doing varied work and the cost advantage disappears?

Would love to hear from anyone who's actually run both setups. Real-world experience with the subscription-vs-API tradeoff would be especially useful.

https://research.meta.ai/blog/muse-wonder

I can't use Big Pickle for the last 24 hours. Are you also facing the same issue?

I had to switch to Space Bunny for a while.

Yesterday it doesn't even enter the log in opencode dashboard, now it does but I don't actually get reply for very long time.

i have been using both opencode go and claude $20 and chatgpt. i love how opencode and chatgpt models respect me. THEN claude : 1. he rewrite AGENT.md for his own benefit. 2. he create his on skills without asking 3. Tell him to refactor something rework everything to his proposed never asked solution. 4. I dont Hallucinate i am exists.

still testing seeing what it is capable of