AutoFlow Robotics

Software Engineering for Process-Driven Systems
SaaS · AI-assisted systems · Operational software

[AutoFlow Robotics @AutoFlowRobotic]

RightSlot ist live.

Nach langer Entwicklung ist die Buchungssoftware für lokale Dienstleister jetzt verfügbar.

Weniger Abstimmung. Mehr Übersicht.
Eigene Buchungsseite. Dein Team. Deine Abläufe.

Jetzt 6 Wochen kostenlos testen → https://t.co/1mlKoUbSZw

[AutoFlow Robotics @AutoFlowRobotic]

@AndrianiGio22 hey

[AutoFlow Robotics @AutoFlowRobotic]

@Rivo__09 @mrtbrgl 🤝

[AutoFlow Robotics @AutoFlowRobotic]

@mrtbrgl Tech & Software development

[AutoFlow Robotics @AutoFlowRobotic]

RightSlot’s launch plan starts here: first users rarely come from a platform checklist.

Start with:
• one narrow customer type
• conversations
• same-day use
• follow-up after the first job
• referrals from users who stayed

Product Hunt can wait.
The first 10 who return cannot.

[AutoFlow Robotics @AutoFlowRobotic]

@002Sindirella I need

[AutoFlow Robotics @AutoFlowRobotic]

@dtitser Yes 🙌

[AutoFlow Robotics @AutoFlowRobotic]

@X_supporttop1 Active

[AutoFlow Robotics @AutoFlowRobotic]

As of today, Nvidia’s careers page lists 2,653 open roles.

A large share is engineering and software — worldwide, including new grads.

So not: “AI replaces programmers.”
More like: “We need more people who know what should be built.”

Source: https://t.co/scVnyaFuzb

Do you think AI creates more engineers — or fewer?

[AutoFlow Robotics @AutoFlowRobotic]

Jensen Huang says something uncomfortable:

Coding isn’t the job.

The job is knowing which problems are actually worth solving.

[AutoFlow Robotics @AutoFlowRobotic]

AI changes how code gets written.

It doesn’t remove the work of finding problems, working as a team, diagnosing failures, and judging results.

That’s why Nvidia expects more software engineers — not fewer.

[AutoFlow Robotics @AutoFlowRobotic]

@X_supporttop1 @9nineOzero FOLLOW

[AutoFlow Robotics @AutoFlowRobotic]

@abmuhammadhamza @X_supporttop1 Active 🌟

[AutoFlow Robotics @AutoFlowRobotic]

@techyoutbe The useful part is the policy check before tools run.
Without that, the rest of the architecture is just a longer path to the same mistake.

[AutoFlow Robotics @AutoFlowRobotic]

@FetchrBots This is the useful layer.
Payment is just the last event.
The real product is the mandate, the limits, and the record of what the agent was allowed to do.

[AutoFlow Robotics @AutoFlowRobotic]

@jerryjliu0 The lock-in is less the model and more the working context.
Skills, routines, and project memory are what make switching expensive.

[AutoFlow Robotics @AutoFlowRobotic]

@EasyClawBot False completion.
The draft looks finished, but the check that should have caught it never ran.

[Rahul @sairahul1]

How to use Claude Code all day without hitting limits.

This setup saves at least 60% of your Claude token consumption — and you never hit the 5-hour limit again.

You only need to set it up once. Here's exactly how:

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STEP 1 — Install the Codex plugin inside Claude Code

Run these three commands:

/plugin marketplace add openai/codex-plugin-cc
/plugin install codex@openai-codex
/reload-plugins

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STEP 2 — Tell Fable 5 to finish the setup

Paste this prompt into Claude Code:

"Set up Codex inside this Claude Code environment. Use the official OpenAI Codex plugin that was just installed. Run /codex:setup. If Codex CLI is missing, install it. If Codex is installed but not authenticated, ask me to authenticate with my ChatGPT account. After auth is complete, verify that Codex works from inside Claude Code. Then confirm that the codex:codex-rescue sub-agent is available. Do not change any project code during setup."

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STEP 3 — Authenticate once

Fable 5 will trigger the Codex setup automatically.

You authenticate your ChatGPT or Codex account once.

After that, Codex runs from inside Claude Code using your existing Codex subscription — no extra cost, no extra setup.

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STEP 4 — Tell Fable 5 how to delegate work

Paste this prompt:

"From now on, use this workflow:

You are the orchestrator.

Use Fable 5 for planning, repo understanding, architecture decisions, task decomposition, and final review.

Use codex-rescue as the executor when a task needs heavy implementation, debugging, test fixing, refactoring, or multi-file code edits.

When delegating to Codex, use /codex:rescue.

Prefer GPT-5.6 Sol medium as the daily driver for implementation tasks.

Keep Codex tasks focused and specific.

After Codex finishes, inspect the result yourself before accepting it.

Do not blindly trust Codex output."

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WHICH GPT-5.6 MODEL TO USE AND WHEN

This is where most people leave money on the table. Not all GPT-5.6 tiers are equal.

GPT-5.6 Sol medium → your daily driver

DeepSWE score: 61. Cost: $1.86. Fewer steps than almost every other model on the benchmark. This is the model doing 80% of the execution work in this setup. Fast, cheap, accurate enough for most implementation tasks.

GPT-5.6 Sol extra high → planning and orchestration

DeepSWE score: 71. Cost: $4.70. Use this when the task needs serious reasoning — architecture decisions, task decomposition, complex debugging. Scores higher than Fable 5 extra high (70) at less than a third of the cost ($13.41 vs $4.70). This is the model that replaced Fable 5 as my planning layer.

GPT-5.6 Terra/Luna → pure execution

Fast. Cheap. No overthinking. Once the plan is locked, this is what runs it. Extremely fast execution, minimal bugs, high quality output on well-defined tasks.

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THE ULTIMATE PLAN: 3-MODEL WORKFLOW

If you want maximum output quality at minimum cost, this is the setup:

Step 1 → Plan with GPT-5.6 Sol extra high
Full task/session/project planning. Architecture. Decomposition. Edge cases.

Step 2 → Critique with Fable 5 high
Find loopholes. Patch loose ends. Challenge assumptions. Fable 5 is at its best here — pure reasoning, no implementation cost.

Step 3 → Execute with GPT-5.6 Terra/Luna
Implement the battle-tested plan. Fast, clean, no waste.

TLDR:
Plan → GPT-5.6 extra high
Critique → Fable 5 high
Execute → GPT-5.6 Terra/Luna

The orchestrator thinks. The critic patches. The executor builds. You review.

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4 PRO TIPS TO NEVER HIT A LIMIT AGAIN

→ Tip 1: Turn this into a skill

Name it Fable-GPT. Call it at the start of every session. One command activates the entire workflow — no re-pasting prompts every time.

→ Tip 2: Use skill + goal for heavy tasks

Goals are best for long-horizon work. Set the goal, activate the skill, let the orchestrator-executor loop run until it's done. Check back when it surfaces for review.

→ Tip 3: Use subagents if you're on the Codex 20x Pro plan

Run 5 to 7 parallel subagents at once. With this setup, you will never hit the 5-hour limit. Each agent works independently on its assigned task while the others run in parallel.

→ Tip 4: Clear context after 4 compactions

Context rot is real. After 4 /compact cycles the conversation quality degrades. Use a /handoff skill before clearing to preserve the critical context — task state, decisions made, what's left to do. Start the new session by loading the handoff file.

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THE FULL PICTURE

Before this setup:
→ Fable 5 hits limits by midday
→ Heavy implementation burns your best tokens
→ You manually switch between tools
→ One model, one pace, one bill

After this setup:
→ GPT-5.6 Sol medium handles 80% of execution at $1.86/task
→ GPT-5.6 extra high outplans Fable 5 at 3x lower cost
→ 3-model critique loop catches every bug before it ships
→ 5 to 7 parallel subagents running simultaneously
→ 60%+ fewer Fable 5 tokens consumed
→ Never hit the 5-hour limit again

One setup. Runs forever.

Save this. Set it up tonight.

[AutoFlow Robotics @AutoFlowRobotic]

@EasyClawBot Verification.
Challenge is useful, but without a separate check that assumes the output might be wrong, the whole loop just confirms itself.

[AutoFlow Robotics @AutoFlowRobotic]

Not personally with banking — but I wouldn’t give full access either.

There are already reports of agents getting out of their sandboxes during tests.
One OpenAI evaluation ended with models reaching real infrastructure outside the intended environment:

https://t.co/UmmerAGQ6Q

Full access is powerful.
I’d still keep hard limits on anything that can move money.

[AutoFlow Robotics @AutoFlowRobotic]

Dalian, China. COVID times.

10 days of hotel quarantine on arrival.
Locked in, but strangely calm.

Back then I mostly thought:
forced practice with chopsticks 😅
The photo has over 7.6 million views now.

Crazy.
Wild period.
Still glad I went through it.

https://t.co/iPQy0tbsIk