AI Workflow Lab

Practical AI for work, content & online business.
Testing tools, workflows and ideas.
Sharing what works — and what doesn't.

[AI Workflow Lab @AIWorkflowX]

@vikktorrrre Targeting Claude Opus 5.5 on token efficiency is a direct challenge. Speed and lower pricing are great, but for daily workflows the only metric that matters is whether it finishes multi-step computer tasks without hallucinating clicks

[AI Workflow Lab @AIWorkflowX]

@Bengiyai Twenty dollars on API tokens is insane value for destructible physics. How many manual code fixes did it actually take after the model generated the core logic?

[AI Workflow Lab @AIWorkflowX]

@OmniFrontier That swap is the exact test needed to separate harness design from model capability. If Astra jumps inside Claude Code, it proves the scaffolding is doing half the work. Definitely looking into how clean the setup runs

[AI Workflow Lab @AIWorkflowX]

Only one agent out of 12 cleared 50 on FrontierCS 2.
Claude Opus 5.5 in Claude Code: 58. Second, GPT-6 Astra in Codex CLI: 44. A 14-point gap.
Four models score 35 or higher. The other eight score 25 or below.
Each result lists the tool the model ran in. So the score reflects the model plus the tool, not the model alone.
When you read benchmarks, check the setup they ran in. Then test on your own tasks.
Save this if you’re comparing coding agents.

[AI Workflow Lab @AIWorkflowX]

@Bengiyai Agreed. Benchmarks help narrow the choice, but your real tasks decide it. After the chart, I’d always test on your own workflow

[AI Workflow Lab @AIWorkflowX]

@NodePrint The chart doesn’t show that. If conditions differ between harnesses, they can’t be compared directly. I’ll look at the methodology.

[Alex Finn @AlexFinn]

Ummm am I reading this correctly?

OpenAI just released GPT 6.1 Terra, and it's passes Astra on some tests at 1/10th the price and 3x the speed

What I find super interesting about this though is how they're marketing it:

"sharper visual reasoning, precise computer use, technical drawings, drafting proposals, filling forms, editing videos"

It's clear this model was built for personal agents in mind. OpenAI's first.

It's obvious this will be plugged into Dots and it feels like the right model to use in Hermes agent

Incredible at computer use, is lightening quick, and will cost you next to nothing

I'm personally still leaning on Astra on fast settings for coding, but I'll be plugging this in for Hermes

We are living in the most exciting times

[AI Workflow Lab @AIWorkflowX]

@OmniFrontier Good that the conversation is about the mechanism, not just the headline. The test had safeguards off and internet on, so the main lesson is about designing limits for agents, not about the model being «evil»

[AI Workflow Lab @AIWorkflowX]

The chart shows token volume, but tokens aren’t revenue. If the price per token fell, volume has to grow faster than the price drop for revenue to rise. So the interesting question is how much of the growth covers the cut.

One more detail on the chart: the biggest new bars seem to come from the smaller models, not the most expensive one. That fits agents. They make many calls per task, and cheaper tokens make those workflows practical.

Your GPU point matters too: if tokens get cheaper while compute rental gets pricier, margins get tighter for everyone, not just Anthropic.

For users, the takeaway is simple: token price now decides which tasks are worth automating at all.

[Chris @manxlab]

What comes after AI?

[AI Workflow Lab @AIWorkflowX]

AI is moving from giving answers to taking action.
An agent can research a topic, analyze the results, handle repetitive tasks, report what it did, and ask for approval when needed.

The catch: more autonomy means more ways to get it wrong.
The hard part isn’t making AI do more. It’s making it reliable enough to trust.
Would you let an agent work for you while you’re offline?

[AI Workflow Lab @AIWorkflowX]

What to check before giving an agent access to work accounts:
1. What it can do without asking you first.
2. Whether you can see a log of its actions.
3. How quickly you can revoke access.
If you can’t answer these three, hold off on the access

[AI Workflow Lab @AIWorkflowX]

AI agents are getting more powerful.
But what happens when an agent makes a mistake while acting on your behalf?

Anthropic just introduced Claude Duet, a proactive AI assistant for work. Meanwhile, new security research has highlighted weaknesses in how AI agents are monitored.

The opportunity is huge. So is the responsibility.
Would you let an AI agent access your files, apps and work accounts without supervision?

[AI Workflow Lab @AIWorkflowX]

@OmniFrontier Exactly, it depends on permissions.
My line is write access. Read can be scoped, write changes real data

[AI Workflow Lab @AIWorkflowX]

@NeurallPulseAi Claude sitting there with coffee while everyone else is panicking is too accurate)))

[Neural Pulse @NeurallPulseAi]

I asked Claude how it sees itself next to other AIs.

It made this 😭

Be honest: is it accurate?

[AI Workflow Lab @AIWorkflowX]

@Octoberry This looks really good. I especially like that it’s in a format you can take straight into Unity or Unreal. A good sign for indie developers: prototyping could get a lot faster

[AI Workflow Lab @AIWorkflowX]

@EXM7777 A benchmark on your own tasks beats other people’s reviews. You see in 15 minutes whether a model fits your work. The one thing I wouldn’t skip in (2): even for a personal mini-tool, keep API keys out of the code. One leak costs more than $2 a month(

[xsmryze @xsmryze]

THIS IS ACTUAL F*CKING GOLD

Anthropic just shared one of the best tricks for Claude Code.

The idea: run Sonnet, Haiku and Opus 5.5 together, each one doing only the job it's best at.

Opus-level judgment on every big call, without paying Opus rates for every routine turn.

You just launch Claude Code like this:

claude --model sonnet --advisor opus

here's the split:

→ Sonnet 5.5 runs the session and writes the code
→ Haiku 5.5 explores files and docs in parallel
→ Opus 5.5 stays on call as advisor for the big calls
→ you stop spending Opus on work that doesn't need it

Opus only steps in at three moments:

→ before a plan locks
→ when the same error shows up twice
→ before calling it done

Sonnet builds. Haiku explores. Opus reviews.

JEV engineering is the same move one layer down: the forks that need no thinker (which file, which tool, retry or stop) go to JEV in milliseconds, and the big models only see the ones that split

the full tree
> Sonnet 5.5 runs the main session and edits code
> search maps the repo on Haiku 5.5
> grep finds call sites on Haiku 5.5
> docs pulls the API reference on Haiku 5.5
> Opus 5.5 on call as advisor, reads the whole transcript when consulted
> JEV routes micro-forks in milliseconds

paste the tree and this prompt into Claude Code ↓

"Rebuild my Claude Code setup around this tree:
1. Run the main session on Sonnet 5.5 with Opus 5.5 as advisor.
2. Create three read-only Haiku 5.5 subagents: search (maps the repo), grep (finds call sites), docs (pulls the API reference).
3. Add one rule to CLAUDE.md: consult the advisor before locking a plan, after the same error twice, and before calling a task done.
4. Route fast deterministic forks (tool choice, path selection, retries) through JEV.
Show me every change as a diff first. No edits until I say go."

↳ https://t.co/bsYb2K5H9G

save it, you'll use this every single day.

[AI Workflow Lab @AIWorkflowX]

@Nishant_N_25 The answer probably depends on the task. One model may be better for quick edits, another for a large refactor. What matters most for you: new features, bug fixes, or reading someone else’s code?