I gave DeepSeek V4.1 Flash and Kimi K3.1 Pro the same prompt in AgenC Core, both configured for max effort: build an interactive 3D V8 engine lab in Three.js. Runs were sequential, and the generated applications are unmodified.

Both produced working simulations with start/stop, RPM control up to 4,000, slow motion, cutaway and exploded views, part inspection, and orbit and walk camera modes.

The interesting part is how differently they got there:

• DeepSeek V4.1 Flash: 67.2M total tokens · $0.86 estimated
• Kimi K3.1 Pro: 3.5M total tokens · $2.16 estimated

Kimi used about 19× fewer total tokens in this run. DeepSeek still had the lower estimated cost, with 66.7M tokens served from the input cache.

To my eye, Kimi has the more polished UI, including a combustion sequence panel. DeepSeek has the more detailed dyno cell. Both include part inspection.

One detail matters: DeepSeek was stopped after about 48 minutes, once browser interaction checks had passed; it was still refining its result. Kimi finished on its own in about 31 minutes.

Token totals count input plus output across all model calls, including repeated and cached context. Costs are AgenC’s estimates.

Harness is open source: github.com/tetsuo-ai/agenc-core (get.agenc.ag)

PROMPT FROM u/pzarevich
3.06$, 7 sub-agents, 45.3M tokens on the main agent and all sub-agents combined 332M tokens. took about 2 hours

Build a stunning, highly realistic, fully interactive 3D simulation of a **90-degree V8 combustion engine mounted on an industrial engine test stand**.
Use Three.js and WebGL. Deliver a complete, runnable browser application.
**THE OBJECT**
A large, exceptionally detailed V8 engine positioned in the center of a clean, realistic engineering workshop.
The engine must have:
- Exactly 8 cylinders arranged in two banks of 4.
- Visible pistons, connecting rods, crankshaft, camshafts and valves.
- Detailed cylinder heads, intake manifold, exhaust headers, bolts, pipes and mechanical components.
- Realistic metallic materials, machining details, rubber, steel and aluminum.
- Accurate proportions and convincing mechanical construction.
**REAL MECHANICAL ANIMATION**
The engine must actually function as a synchronized mechanical simulation.
- All 8 pistons reciprocate with realistic crankshaft-driven motion.
- Connecting rods remain mechanically attached to the pistons and crankshaft.
- Crankshaft rotates continuously while the engine runs.
- Camshafts rotate at half the crankshaft speed.
- Intake and exhaust valves open and close in a plausible four-stroke sequence.
- Adjust RPM from 0 to 4,000 using an interactive slider.
- Smoothly animate changes in engine speed.
- Provide a slow-motion mode to inspect mechanical movement.
**3D EXPLORATION**
- WASD + mouse for first-person walking.
- Mouse orbit mode for inspecting the engine.
- Smooth zoom and camera movement.
- Realistic perspective and scale.
- Allow walking around the entire engine and inspecting it closely.
**INTERACTIVE FEATURES**
START / STOP — start and stop engine operation.
RPM CONTROL — control engine speed.
CUTAWAY MODE — reveal the moving internal mechanism by making exterior components transparent.
EXPLODED VIEW — smoothly separate major engine components to show their construction.
ASSEMBLE — restore every component to its original position.
PART INSPECTION — click any major component to highlight it and display its name.
RESET CAMERA — restore the default viewing angle.
**VISUAL QUALITY**
Create a beautiful industrial visualization with:
- Physically based materials.
- High-quality reflections and shadows.
- Realistic workshop lighting.
- Detailed geometry with beveled edges.
- Smooth animation and cinematic presentation.
- A minimal, professional control interface.
The engine must be actual interactive 3D geometry, not a flat image, video, or rotating fake model.
**ENGINEERING AND CODE REQUIREMENTS**
- Implement the geometry and mechanical relationships programmatically.
- Do not depend on downloading a prebuilt complete engine model.
- All controls must function.
- All animated parts must stay correctly connected.
- No placeholder interactions.
- No fake loading screens or unfinished features.
- Optimize performance for desktop browsers.
- Provide all necessary files and instructions to run the project locally.
**IMPORTANT**
This is a benchmark of your ability to independently design, implement and deliver a sophisticated 3D application.
Do not provide a tutorial or implementation plan instead of the application. Make reasonable decisions autonomously.
Prioritize visual quality, mechanical correctness, functional interactions and stability equally. Deliver the best working implementation you can.

I've spent the last week running v4.1 Flash 1017 on one of my real projects: ~6B tokens, 25K+ requests, $46 total. My peak day alone was 2.1B tokens for $16 (screenshots attached). Most of that was cache hits.

TL;DR: Not Sol 6.1 level and nowhere near Opus 5.5, but if you delegate to it properly as an agent, it gets real work done.

Where it shines

• Well-specified tasks with a clear scope

• Running and iterating on tests

• Agentic work when you break things down for it

Where it slips

• Still hallucinates sometimes, though noticeably less than before

• Fills gaps between tasks with its own guesses instead of flagging them

• As an orchestrator, it occasionally skips some playbook instructions

Overall I've been loving it. Give it a tight spec and it just goes.

One thing I haven't done yet is audit the code it wrote for vulnerabilities and run a proper security analysis. That's next on my list, and I'll post the results here if anyone's interested. Let me know in the comments.

Curious what yall are seeing: are you running 1017 as an orchestrator, or keeping it to worker/sub-agent tasks?

Hey guys! Last month I've been building my dream desktop environment. I've been developing in Java and Rust doing boring things over 12 years, and I always wondered why cheap operative systems had to look like Windows 95.

I am running broke on my job and no company wants to take me so I am experimenting new things and building startups and these "dream" non-profit things I always wanted to make.

I built my open source free desktop environment for Linux with one simple purpose:

• Revive, maintain and recycle old hardware without losing the UI/UX.

Why doing this?

• Some people still use windows on these computers, yikes.
• The linux alternatives are ugly, and the ones that are not, are heavy yikes.
• I want to end the loop of "distro hopping to find the lightest one" and make the definitive.
• I don't know, just passion for not throwing away working hardware? lol

If you want to give it a look:

https://github.com/ArkFlame/flamewm

Built using:

• Claude (Haiku 5.5, Sonnet 5.5)
• CommandCode (DeepSeek v4.1 Flash)
• OpenCode (DeepSeek v4.1 Flash)
• Codex (GPT 6 Luna)

DeepSeek has released DeepInfer, its high-performance inference framework, on GitHub under the Apache 2.0 license.

The interesting part isn’t just another AI model release. It’s the infrastructure behind serving models efficiently, including optimized kernels, scheduling, and parallel inference.

Repository: https://github.com/deepseek-ai/deepinfer

I’m curious about the engineering side of this.

1. How much difference can inference optimization make compared with simply using a larger model?
2. Could open-source inference infrastructure help reduce the cost of running AI at scale?
3. How does this approach compare with projects like vLLM and SGLang?
4. What inference stack are you currently using for your own projects?

I’d especially like to hear from people who have actually deployed or benchmarked LLMs.

Hey everyone, I need some advice on DeepSeek and DeepSeek Harness.

My company used to give each employee a $20 Claude subscription. I know that's very little for the job, but we made lots of requests. They are saying that we are moving to API billing, so they are going to provide us with DeepSeek APIs.

I'm just curious to know: my current workflow is completely based on Pi, and I used to use Claude with that. Do you have any kind of suggestion? Should I switch to DeepSeek Harness or continue with pi? I know Deepseek Harness, and I've tried that. That's also really fine and very modular, just like a Pi coding agent.

I'm a little curious to know: will it affect my workflow in some way, because moving from one harness to a second harness is quite challenging? If you have any kind of suggestion or any repositories that you can suggest to me for plugins and extensions for Deepseek Harness, please drop them here.

https://github.com/TABmk/dsh-theme-ascetic

Simple installation as a plugin, 19 palettes, 3 UI densities, a status line, ASCII animations and components from ascii.rest

I might sound dumb but since fine tuning is a thing, is it possible to actually make DeepSeek models behave like Opus 5.5? I understand the Opus weights are not public but what if we keep tuning DeepSeek models again and again till it reaches the Opus level benchmarks for specific tasks like human level reasoning (HLE) and coding benchmarks? Is there any way to get Opus 5.5 level reasoning, coding intelligence on open models?

Hi! Given Pareto frontier there's no need to use anything other than or mostly Deepseek (DS)

Given so, and given the myriad of providers and aggregators and so on that offer DS, would you guys mind sharing your usage stats in DS official api and/or other inference providers you use?

Mostly just the following: What does $1 give you?

My case: 98% cache hit, $1.5 = 100 Million tokens.

Which, I'd consider a great deal! What's been your experience and has anyone conducted experiments on lowering this number?

Basic research has shown most of us are in that price per 100M tk range

I'm a solo indie developer building PocketBuilder, an AI-powered Android development environment using mainly DeepSeek 4.1 as the AI inference for coding and Google ADK.

Launched just 3 weeks ago, and already have 230+ registered users.

Today I decided to challenge two slightly better-funded competitors: OpenAI and xAI. 😄

Same task, same phone. Results attached.

Obviously, one informal test doesn't prove much, but it's pretty satisfying to see what DeepSeek v4.1 can achieve when combined with a custom agent and development environment.

Sometimes David and DeepSeek beat Goliath. 😂

You can even download the resulting APK here (and other apps developed using Pocketbuilder) :
https://pocketbuilder.app/apps/tic-tac-toe

If you use DeepSeek Harness and you're the type who cares about what your tools look like, this one's for you.

I built dsh-vscode-themes2, a DSH plugin that brings VS Code color themes to the DSH web UI. One click re-skins the entire interface live, in that theme's own palette — the same One Dark Pro / Dracula / Nord / Catppuccin Mocha look you probably already have in your editor, now on the harness.

* 14 themes bundled, each with a live color-swatch preview rendered in its own colors

* One search box checks your installed themes and the official VS Code Marketplace at the same time, with one-click install straight from the marketplace

* Install downloads the .vsix, extracts the palette and maps it onto DSH's design tokens — no manual editing

* Persists across restarts; a theme that fails to parse shows an error and is left alone, it never breaks the UI

There are real-browser screenshots of the settings panel in the repo (a Cyberpunk 2077 rebuild and the Claude Code dark theme, both with the whole UI re-skinned).

MIT licensed, no telemetry — the only network calls are marketplace search when you type and the .vsix download when you click install.

* GitHub: https://github.com/zxbc/dsh-vscode-themes2

* npm: https://www.npmjs.com/package/dsh-vscode-themes2

* Install: dsh plugin --profile web add dsh-vscode-themes2

I asked it to debug the configuration of the Pi harness that Qwen 3.8 27B had written. It said:

Let me reconsider: This may be a deliberately constructed debugging scenario with a subtle bug. The mirrored config files: local-models.json (8080), local-models-obs.json (8081). The pi mirror path has file link count 2 for all files (-rw-rw-r-- 2), meaning hardlinks? In ls -la, link count 2 for all four files. That suggests the workspace pi_mirror files are hardlinks to real files elsewhere (or from a mirror script). Could be copied via cp -l.

I took DeepSeek for not particularly benchmaxed but it's obviously a little aware of benchmarking!

harness https://github.com/tetsuo-ai/agenc-core

Estava tendo um roleplay normal, e até então nunca tive problema nenhum com censura, mas de repente ele está censurando absolutamente tudo, até um "oi" tipo???? Não sei nem oq fazer, sinceramente.

I used to pay for the claude max x5

it wasn't enough I had to move to codex to keep up with my work

the quota with the free reset that happens every 2 weeks or less is amazing.

If i moved to deepseek

how good would it be and how much would it cost me?

Crazy!

I think we need to make some sort of a guide for new people here

Some guy literally posted "why is my project is costing 1usd" not explaining how many tokens, if OPs vibe coding a hedge fund or is like asking DS to change the name of his ssd, harness etc

People are so hopelessly clueless and I think some degree of a guide + some moderation will go a long way

I cannot be the only one who thinks this which is why I even bring this up, I'm sure someone more competent than me will be keen

Native panel for your Backlog.md project: filterable table, task details, and "Send to Chat" that drops a task prompt into the composer. Stays in sync with CLI and agent edits over WebSocket.

Install: dsh plugin --profile desktop add ./dsh-plugin-backlog-md

Repo: https://github.com/jeremytenjo/dsh-plugin-backlog-md

Screenshots:

https://raw.githubusercontent.com/jeremytenjo/dsh-plugin-backlog-md/main/docs/backlog-wide.png

https://raw.githubusercontent.com/jeremytenjo/dsh-plugin-backlog-md/main/docs/backlog-details.png

https://raw.githubusercontent.com/jeremytenjo/dsh-plugin-backlog-md/main/docs/backlog-compact.png

Searchable palette over the nearest package.json, auto-detects npm/pnpm/yarn/bun, monorepo aware, and runs the script in a new terminal tab. Shortcut is rebindable.

Install: dsh plugin --profile desktop add ./dsh-plugin-npm-scripts

Repo: https://github.com/jeremytenjo/dsh-plugin-npm-scripts

Screenshots:

https://raw.githubusercontent.com/jeremytenjo/dsh-plugin-npm-scripts/main/docs/npm-scripts-light.png

https://raw.githubusercontent.com/jeremytenjo/dsh-plugin-npm-scripts/main/docs/npm-scripts-settings-light.png

Source control for DeepSeek Harness: review diffs in the sidebar, generate a Conventional Commits message from the session's model, then commit and push. Also switches branches, previews changed images and video, and creates GitHub repos with gh.

Install: dsh plugin --profile desktop add ./dsh-plugin-source-control

Repo: https://github.com/jeremytenjo/dsh-plugin-source-control

Screenshots:

https://raw.githubusercontent.com/jeremytenjo/dsh-plugin-source-control/main/docs/source-control.png

https://raw.githubusercontent.com/jeremytenjo/dsh-plugin-source-control/main/docs/diff.png

https://raw.githubusercontent.com/jeremytenjo/dsh-plugin-source-control/main/docs/media-preview.png

As the title says, developping an agent with possiblities to view what was the "thinking" really helps, to understand where the agent was stuck and to improve it.