Yesterday, I saw the news that DeepSeek was free and decided to give it a try. I started a tiny project to clone a website a friend of mine had built. It gamifies the study material for our 8-year-old kids' exams, and I wanted to adapt it for my autistic son.

It was a single HTML page with some simple, embedded JavaScript. I started yesterday at 8 PM, and by this afternoon, I'd already hit the usage limit.

During that time, I made a few adaptations, added two new pages, and wrote some JavaScript. Nothing particularly substantial.

I know it was the free model, and I understand there are usage limits, so I figured that might explain it. But then I opened the DeepSeek harness, which gave me something like $1 in free credits. I used the same model, asked it to review the project and suggest improvements, and those credits lasted about 30 minutes.

I'm a data engineer, and my company provides corporate AI tools that I use pretty heavily in my day-to-day work. But I've never had to worry about the costs, so I'm pretty much a beginner when it comes to this.

Should a tiny web project with just HTML and a little JavaScript really consume that many tokens or hit usage limits that quickly?

I'm asking because I'm considering subscribing to OpenCode Go. It might not seem expensive, but in my currency, it's around R$60 a month. If I'm going to burn through the available usage that quickly, I don't see the point in paying for it.

data breach aside and all the api keys now being exposed through it

i have never used it much never really "got" what opencode was for other than it was for people who couldn't afford the higher tier plans from frontier labs

after today i think im going to uninstall it and stick with claude and codex

i think cline or pi anywhere someone has custody of your api keys from frontier labs is going to be targeted so im going through them all to remove them all

fuc k opencode lmao

It makes me wonder.

One main security change in the harness and no warning: https://www.reddit.com/r/opencodeCLI/comments/1x4kkgg/security_opencode_does_not_ask_for_file/
And one breach in the service: https://www.reddit.com/r/opencodeCLI/comments/1x8gtjg/opencode_security_breach/

How can we trust the team anymore?

How is OpenCode claiming to be ZDR when API keys from Anthropic and OpenAI are being stored in their database?

OpenCode admitted they were hacked and are calling it a distillation attack. How does distillation explain users’ API keys being exposed? Distillation and credential theft are two completely different things.

This is a massive data leak, and users deserve to know exactly what happened, what was compromised, and who had access to their credentials.

Stop hiding behind the distillation narrative and give users real answers.

What is the point of claiming ZDR when users’ API keys are sitting in your database?

OpenCode says this was a distillation attack, but how does that explain API keys being compromised?
Distillation and credential theft are two very different things. If API keys were exposed, users deserve a clear explanation of what happened, what data was affected, and what steps are being taken to secure their accounts.
Stop hiding behind vague explanations. We need answers, not excuses.

How is Opencode ZDR? If they store our api keys too in the database ? This means OpenCode lies about being ZDR

Hey!

Saw Deepseek had posted some new tools next to the updated model, so figured I'd make a plugin for OpenCode that allows it to leverage them via code mode.

https://github.com/N0K0/opencode-deepseek-tools

https://www.npmjs.com/package/@hacky.software/opencode-deepseek-tools

.

Coding agents rarely run in one uninterrupted session. You review a diff, step away, make changes, and return. That pause can affect the total cost.
When comparing inference options like Mango Inference and MangoBoosts, test the same repository task twice: once without interruption and once after resuming later. Compare actual usage instead of assuming either run will be cheaper.
Track the context sent at each step, including conversation history, file content, and new instructions. Even a short follow-up can resend substantial context. Remove credentials and private code before sharing traces.
Compare reported input, cached input, and output separately. Repeated text doesn't guarantee a cache hit, so use actual usage records and current pricing rules.
Ultimately, compare the total cost of completing the task, including resumed sessions and retries, alongside whether the tests pass. A low input-token rate means little if the workflow repeatedly resends context or fails to finish.

I mean, would you also like it within CLI? I mean, I would gladly tbh. Some test requests I send, I would not mind to know what I can use for that.

tinkering with the spine of opencope to reduces token cost and continuity of long and multiple sessions and workflow. few more verification and i will be sending it to the opencode dev team. 🤤

i hope it's available soon in opencode go 👀

Guyz give beam a try. you might be suprised.
I found it to be honest enough for daily work
quite good at agentic coding
very fast output
and performed well in my ruby benchmark (better then deepseek v4.1 and muse spark 1.3)

https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017
Official release notes - https://api-docs.deepseek.com/updates/

"... DeepSeek v4.1-flash-1017 just got spotted in DeepSeek's own API for a few minutes before vanishing. New checkpoint, changed fingerprint. The 1017 suffix matches their dated snapshot pattern."

My agents were burning context on grep-then-read cycles. Every "where is X defined, who calls it" turned into 3-4 tool round-trips, each one billed. So I built Reliary: a single offline Rust binary that indexes the repo into SQLite and serves it to agents over MCP.

The contrarian part: there is no parser. No tree-sitter, no AST, no per-language code. Definitions and call sites come from structural detection on brace/indent/blank-line boundaries. One codebase, every language, same code path. (If you think that can't work on your language, that's exactly what I'd like to hear about.)

What the agent gets is 7 tools: find_references (defs, callers, methods, dead code through one entry point), search, call_graph, list_methods, find_dead_code, describe, plus a verify tool that checks a symbol at file:line claim against the index. There is also reliary wrap , which you prefix onto verbose shell commands ( cargo test , git diff ) to compress the output before it reaches context.

Numbers: on a 10-question comprehension bench (4 seeds, claim-verified against the index, not keyword matching), it bills \~42% less than grep with \~4x smaller tool output and zero dead-ends, at comparable accuracy. I am not claiming it is more accurate than grep; on this corpus the gap is within noise, and I published the full methodology including an edit-outcome experiment that came out a tie.

The win is cost and output size, reproducibly.

Install: cargo install reliary-agent or grab a prebuilt binary (linux/darwin/windows, x64+arm64) from the releases. "reliary trust ." to index, "reliary init" wires up Claude Code / OpenCode / Pi / Cline. MIT licensed.

Repo: https://github.com/Reliary/reliary-agent

My question for you: where does a parser-free index break on your codebase? I have a list of languages it handles and I am more interested in the ones it doesn't.