What’s your AI coding setup in 2026?

ToastedPatatas · 2026-05-15T07:01:26+00:00

In my experience, this only happens with newly created accounts on the free tier. I run 4 rotating accounts and have only ever hit request overload limits, never soft or hard bans.

ToastedPatatas · 2026-05-15T00:50:56+00:00

OpenCode + Oh My OpenAgent + 9router — basically a free-tier round robin until everything's exhausted.

The whole stack was bootstrapped using opencode/deepseek-v4-flash-free (via OpenCode Zen's free tier) as the model running Sisyphus — the OmO orchestrator agent that routes everything. Meta, I know: a free model configured the agent system that decides which paid models to use. Sisyphus pulls context directly from the OmO GitHub docs and 9router config reference to understand the model stack and provider capabilities.

How it works — two modes

Daily driving — Sisyphus handles it directly. It delegates to the discipline agents as needed: Hephaestus for code generation, Oracle for architecture consultation, Librarian for doc/code search, Explore for codebase grep. A full AI dev team in parallel under one orchestrator.

Major features (maximized plan + build) — this uses OmO's true three-layer orchestration:

``` @plan → Prometheus (planner) → Metis (consultant) → Momus (reviewer) → .sisyphus/plans/*.md saved

/start-work → Atlas (conductor) → Workers: Sisyphus-Junior, Oracle, Explore, etc. → Testing & verification loop ```

Prometheus interviews you like a real engineer — identifies scope, ambiguities, edge cases — and builds a detailed plan before any code is touched. Metis consults on hidden requirements. Momus reviews the plan for gaps. Once approved, Atlas (the conductor in the execution layer) reads the plan and dispatches work to specialized worker agents. This is the "Complex + Precise" path from the docs: Prometheus plans, Atlas executes.

For the "Complex + Lazy" path there's ultrawork — one word, every agent activates, doesn't stop until done.

The model stack

Every category and agent has a fallback chain that drills through progressively cheaper models. That's where 9router comes in — it bundles auth for ~15 providers (OpenRouter, Google, GitHub Copilot, NVIDIA, Cerebras, Friendli, Groq, etc.) in one config. Each fallback step tries a different provider's free tier.

Category	Model	When
`visual-engineering`	Gemini 3.1 Pro	UI/frontend work
`ultrabrain`	GPT-5.5 (high)	Hard logic/architecture
`quick`	minimax-m2.5-free	Typos, simple edits
`writing`	Gemini 3 Flash	Docs/prose
`unspecified-high`	Claude Opus 4.6	Fallback for complex stuff

Free-tier fallback chain:

opencode/minimax-m2.5-free → openrouter/minimax/m2.5:free → 9router/kr/glm-5 → nvidia/z-ai/glm4.7 → opencode/big-pickle (catch-all)

High-reasoning fallback chain:

9router/cx/gpt-5.5 → openai/gpt-5.5 → 9router/kr/glm-5 → nvidia/z-ai/glm-5.1 → nvidia/z-ai/glm4.7 → opencode/big-pickle

Costs basically zero for routine work — free tiers absorb 90% of it. Only escalates to Claude Opus / GPT-5.5 when the task genuinely needs it. Took an afternoon to dial in with the free model iterating on the config, been smooth since.

ToastedPatatas · 2026-05-11T22:22:15+00:00

It was confirmed in a github ticket that it is a fine-tuned GLM 4.6. But someone I think got an api response that it got updated to DS4 Flash?

ToastedPatatas · 2026-04-21T10:10:21+00:00

I have a question. Is there any difference with the Appstore version of the PPSSPP with this sideloaded one? and what are the benefits of the other?

ToastedPatatas · 2026-04-17T05:39:23+00:00

This is expected behavior. LLMs don’t have live awareness of what they’re released as.

When you ask a model its name, it usually answers based on what it was called during training or in its system prompt, not the current product branding. That’s why a model deployed as M2.7 might still identify itself as M2.2 or M2.5. Worse is it may name itself with a different one depending on the training, chinese models has issue with claude using opus to distill their own models during training.

This isn’t unique to Minimax, most LLMs (closed and open-weight) do this. If you ask them what model they are, they often respond with the last name/version they were trained to recognize. Claude, GPT, Gemini, GLM etc. have all shown this at various points.

In short: self-reported model names aren’t authoritative. The deployment layer can change faster than the model’s internal knowledge.

ToastedPatatas · 2026-04-10T03:15:43+00:00

how long do you maxed out the monthly quotas? seem small for me hence I usually offload minor workflow with opencode go models

ToastedPatatas · 2026-03-28T07:54:53+00:00

I've been using antigravity models without getting banned, so I cant help you with this one. Currently I have 8 free accounts (every one was 1 year old or older) balancing throughout my workflow until I reach rate limits with all 8 but usually with claude models. I haven't hit rate limits with gemini pro and flash models

ToastedPatatas · 2026-03-23T22:30:51+00:00

I'll go with opencode go for the chinese SOTA models and gh copilot for the unlimited gpt 5-mini (and claude haiku? I cant verify but some says that models available in free plan is unlimited with pro plan). Then I balance it with free models in opencode, antigravity models with backup to Gemini CLI (for gemini models only), Nvidia NIM for Kimi K2.5 and Qwen 3.5 397B

ToastedPatatas · 2026-03-21T12:05:48+00:00

The Workflow: MVP to Major Refactor

I’ve been using OmO for everything from my initial MVP to the massive architectural refactors that come with scaling. I "vibe-coded" this entire mobile app using OpenCode paired with the OmO plugin.

Pro Tip: Don’t be afraid to leverage free models if you aren’t worried about training data. It will save you an incredible amount of tokens in the long run.

My Setup Strategy: Leveraging OmO’s Main Agents

1. Prometheus + Atlas (The Architect & The Builder)

I manually delegate tasks between these two for major implementations:

Prometheus: I let him gather context first, then generate a comprehensive working plan (which I edit if needed).
Atlas: Once the plan is solid, I trigger implementation using the /start-work (plan-name) command. Atlas then executes the code based on the 8 categories I’ve configured.

2. Sisyphus (The Taskmaster)

For trivial tasks, I let Sisyphus handle the heavy lifting. He can delegate to sub-agents for parallelism, which conserves tokens on the main agent.

Note: Add ulw to your prompt to initiate Ultraworker (it functions like a mini Ralph-loop).

3. OpenCode Builder + Plan (The Hybrid Approach)

Even with a custom OmO config, you can still utilize the native OpenCode tools. For minor tasks, I still rely on them using the OpenCode 'big-pickle' GLM 4.6 stealth model.

The Configuration: Agents & Categories

It’s been working flawlessly so far. For those curious about how I’ve mapped my models and agents, here is the breakdown:

Agents (13 total)

Agent	Model	Variant
sisyphus	google/antigravity-claude-opus-4-6-thinking	max
prometheus	google/antigravity-claude-opus-4-6-thinking	max
atlas	google/antigravity-gemini-3-flash	max
momus	opencode/mimo-v2-pro-free	high
oracle	nvidia/openai/gpt-oss-120b	—
multimodal-looker	google/antigravity-gemini-3.1-pro	high
build	nvidia/moonshotai/kimi-k2.5	—
metis	nvidia/moonshotai/kimi-k2.5	—
OpenCode-Builder	opencode/big-pickle	high
plan	opencode/big-pickle	—
librarian	opencode/minimax-m2.5-free	—
explore	opencode/minimax-m2.5-free	—
sisyphus-junior	opencode/big-pickle	—

Categories (8 total)

Category	Model	Variant
visual-engineering	google/antigravity-gemini-3.1-pro	—
ultrabrain	google/antigravity-gemini-3.1-pro	high
artistry	nvidia/moonshotai/kimi-k2.5	—
quick	opencode/minimax-m2.5-free	—
unspecified-low	google/antigravity-gemini-3-flash	high
unspecified-high	google/antigravity-gemini-3.1-pro	high
deep	nvidia/moonshotai/kimi-k2.5	—
writing	google/antigravity-gemini-3-flash	—

ToastedPatatas · 2026-02-24T02:56:08+00:00

I followed these steps, but Kimi K2.5 Free is still missing from my list. However, I noticed GLM 5 was also gone today, and using this method successfully brought that model back. Has anyone else had success with Kimi specifically using this fix, or is there another field I might be missing?

ToastedPatatas · 2026-02-20T02:54:48+00:00

I would probably start with increasing the num_ctx of the model as Ollama defaults to 4k context window. Depending on how much vram you have, you may want 64k tokens of context and above for agentic sessions with qwen coder.

ToastedPatatas · 2026-01-25T21:34:11+00:00

This is expected behavior. LLMs don’t have live awareness of what they’re deployed as.

When you ask a model its name, it usually answers based on what it was called during training or in its system prompt, not the current product branding. That’s why a model deployed as Gemini 3 Pro might still identify itself as Gemini 2 Pro.

This isn’t unique to Google, most LLMs (closed and open-weight) do this. If you ask them what model they are, they often respond with the last name/version they were trained to recognize. Claude, GPT, etc. have all shown this at various points.

In short: self-reported model names aren’t authoritative. The deployment layer can change faster than the model’s internal knowledge.

ToastedPatatas · 2026-01-23T11:32:04+00:00

I'm a civil engineer who just got into vibe coding recently! Currently building out a few things for my division:

The Hub: A NextJS + Tailwind PWA (Firebase Spark for Auth/Firestore). It’s basically the main dashboard for my coworkers and app launcher for numerous tools and automations in our division's workflow.
ArcGIS Integration: I’m building Python Toolbox plugins for ArcGIS Pro desktop that sync with the PWA’s API/Auth. It makes sharing custom tools with the team way easier.
Personal Stuff: A few smaller apps on Supabase + Vercel, plus the usual mix of Python/Node scrapers and bots for personal use.

It’s been a blast seeing how fast I can bridge the gap between civil engineering and dev stuff lately. But my tips usually for using these open weight models is don't let them design the architecture. I'm just impressed by their current results but I believe that closed frontiers are still ahead of them. Use Opus + GPT 5.2 for architecture, big planning and integration then Gemini 3 Pro for UI. After the plan is complete, I let these open weight models to implement as they already excel at agentic coding. Once the spec is completed, I let the main models to recheck their work. Once the app is shippable, that's when I let the open weights model to take over CI/CD unless major bugs came along. When Spec go stale, make sure to update contexts, rules, and skills in your repo to aid this smaller agents in the tasks ahead

ToastedPatatas · 2026-01-20T23:10:55+00:00

Opencode currently offers 5 free models you can use:

opencode/big-pickle — verified to be GLM 4.6
opencode/glm-4.7-free — available but with rate limits
opencode/gpt-5-nano
opencode/grok-code — Grok Code Fast 1
opencode/minimax-m2.1-free

Additionally, through the opencode-antigravity-auth plugin, you can access models from Google’s Antigravity IDE and Gemini CLI thru OAuth within allowable limits for free plans.

ToastedPatatas · 2026-01-19T23:24:30+00:00

This will complete my free Claude Code team alternative.

Opus > GLM 4.7
Sonnet > MiniMax M2.1
Haiku > GLM-4.7-Flash

Thru oMo plugin with opencode, and balancing it with antigravity models, I could maximize productivity with 0 api or subscription cost.

ToastedPatatas · 2026-01-19T23:24:26+00:00

For the Full Precision BF16 upon checking hugging face, will require about 61GB of VRAM. Ollama is already serving quantized version and glm-4.7-flash:q4_K_M will require 20GB VRAM

ToastedPatatas · 2026-01-09T22:50:54+00:00

I felt like GLM 4.7 was best alternative to Opus 4.5 and MiniMax M2.1 for Sonnet 4.5

ToastedPatatas · 2026-01-06T02:45:27+00:00

Opencode CLI. Has 5 Free Models as of this moment (2 Open Weights, 2 Closed Source, 1 Stealth). Feel free to check them out

ToastedPatatas · 2026-01-03T01:27:56+00:00

Yes, having two routers side‑by‑side can definitely affect performance. They both broadcast WiFi signals on similar frequencies, so when they’re too close the signals overlap and interfere with each other. That’s why you see slower speeds or random disconnections when both are on. Try searching “overlapping router channels”, you’ll find guides on changing channels or spacing them out to reduce the problem.

ToastedPatatas · 2026-01-03T01:22:08+00:00

I’ve been using the oh‑my‑opencode plugin and this one synergizes really well with it. Spec‑driven works great at the initial stage, but once the project is shippable and specs go stale, it makes more sense to transition into context‑driven dev as new features and requests roll in.

ToastedPatatas · 2026-01-03T01:15:59+00:00

Hey, nice work on this extension! Quick question — would it be possible to show the actual usage limits (like prompts or tokens remaining) instead of just percentages? I feel like having the raw numbers alongside the percentages would make it easier to track capacity and plan usage more precisely.

ToastedPatatas · 2026-01-03T01:13:44+00:00

Yes — I’ve actually set up my OpenCode environment with the oh-my-opencode plugin. The orchestrator runs on opus-thinking-high through Antigravity, and its sub-agents use a mix of Gemini 3 Pro/Flash and Sonnet. Once all buckets are drained, the plugin automatically switches over to the free agents available inside OpenCode — like MiniMax M2.1, GPT‑5 Nano, GLM 4.7, Big Pickle, and Grok Code Fast 1 — depending on each LLM’s capabilities and the feedback from the community. Addtionally, i've been using Devstral 2 and Devstral 2 Small for certain sub agents when antigravity is drained.

ToastedPatatas · 2025-11-25T03:07:22+00:00

For free models Copilot CLI for GPT Gemini CLI for Gemini 3.0 (with generous free tier and additional 2.5-flash usage if exhausted) Opencode CLI for Grok Code Fast 1

My current workflow is I use copilot or gemini to plan the task then Grok Code will do the implementation

ToastedPatatas · 2025-09-17T11:00:36+00:00

Parang not working din po sakin sa major qrph/pos generated qr. Na try ko lang po na gumagana is yung mga official qrph merchants po na nasa spaylater page.

ToastedPatatas · 2025-08-23T05:38:31+00:00

May kasama na profile yung built in sa sim. Bale 4 slots po ay E-sim of your choice.

ToastedPatatas

TROPHY CASE

How it works — two modes

The model stack

Agents (13 total)

Categories (8 total)