taoh

I’d treat a vibecoded agent like an untrusted CI job, not like a junior employee: repo-scoped identity, read-only by default, no inherited Actions token or production secrets. Any operation that turns a read into a write should require approval outside the agent’s control and produce an auditable diff. Network egress belongs in the boundary too. Read-only access is not much protection if the agent can send everything it reads to an arbitrary endpoint.

The source-retention detail seems more important than the model list: if an email is compiled into a wiki and later deleted, or access is revoked, how quickly does that fact disappear from the personal or company wiki? Is the wiki rebuilt from current source state, or are removals handled as a separate event?

The important detail is whether approval binds to the exact proposed action, including the recipient, repository, issue, or data being sent, rather than just “allow Gmail” or “allow this endpoint.” How granular are the gateway policies for APIs where read and write actions share the same host?

The hard part with these protocols is usually state, not packet formats. Does the spec cover initialization and feature negotiation across different firmware versions, or is that still inferred from one device’s traces?

I don't follow him. Sometimes he appeared on my timeline and I skimmed through. I don't agree or disagree with him strongly. There are some companies that will use or avoid certain products or people. Thus there was my question. If the question looked political, it wasn't my intention. Thx.

I was merely asking a question. I'm not a native English speaker so if my comments sounded offending, I didn't mean it. I don't follow Musk on X. I have no political interests. I read news about SpaceX, Tesla, and X and that's it. I'm focusing on building my own product and company. Just somehow curious when I read the previous comments so I asked.

My feeling too. The foundation models and harnesses are commoditizing eventually because there's no stickiness nor algorithmic supersecrets.

Paseo has this as well

"Over the last few months, we've noticed a significant shift in how people build. As AI models become more capable at reasoning, we've noticed that developers are increasingly relying on new coding agents such as Claude Code/OpenClaw to handle complex tasks." is true when your primary users are developers. But no-code workflow is still valuable at scenarios where determinism is required.

I'm building an agentic compliance platform where every procedure needs to follow company policies and operated on the rigid plan, but the procedure itself is different for every customer. Coding agents could in principle generate the necessary workflow for each company, but they need to integrate with millions of other applications in the company, easy to review and modify.

We built our own workflow builder because every other existing solution is too complicated and doesn't meet compliance requirements. The workflow builder itself was coded by Claude, and it's working wonderfully.

So flowise's shutdown is because their business is targeting a specific sector which doesn't need it anymore, not because workflow builder itself is not useful.

Author here. Built this with Claude Code. Happy to answer questions.

Congratulations on your launch! 4 years of work is certainly remarkable perseverance.

The sync engine feature looks very interesting to me. There have been quite a few products available on the market today, but none has achieved a dominant share yet. So if this is your main strength, I'd like to see more demos built local first.

Curious if you considered shipping the engine itself as a standalone infra piece.

I'm a vercel customer, and I like using vercel AI SDK and Chat SDK. But I found myself moving away from vercel and next.js whenever I start a new project. I wish they maintain the technical standards while achiving commercial success.

Thanks for sharing this experience. Does it cost a lot of token in the deep analysis - which will make the $100 plan much quicker to drain all budgets.

Does GPT-5.4 pro give a much better result in some circumstances? What're their typical uses in your experience?

Congratulations! The difference between pure agentic exploration and deterministic steps is spot on. Runbooks give ops more confidence on the data exploration and save time/context.

Curious how much savings do you observe from using runbook versus purely let Claude do the planning at first. Also how the runbooks can self heal if results from some steps in the middle are not expected.

Have been using it in Claude Code with Max Plan for one day. The rate of acceptance is noticeably higher.

Hi, I'm working on something tackling this problem. Do you mind if I contact you for more discussions?

It should be pretty easy to export to MP4, but using SVG will be lighter and faster, which is why we created the dg tool to automatically export to SVG. Please see my previous comments if interested

We use termsvg to convert cast to svg automatically. The tool is open source: https://github.com/DeepGuide-Ai/dg.

We made a tool using termsvg: https://github.com/DeepGuide-Ai/dg. It'll use recorded sessions and execute the non-interactive sessions during CI.

We use asciinema to record CLI tools terminals and add the recordings as svg to our README. We also use the recordings to replay as part of our CI. works great!

Using SVG for Demos is much better than GIFs or Videos due to the lightweight nature. We have created a tool to make the recording and sharing CLI tool demos much easier: https://github.com/DeepGuide-Ai/dg . Simply call `dg capture` and it generates the svg and content ready to paste to README. An added benefit is it can be used for CI validations. It utilizes termsvg under the hood. Would love your comments.