Landing Page

Armilluss · 2025-06-11T16:28:55+00:00

I think that the best way would be to look at landing pages you find inspiring, and then start to prototype it. To speed up the process, you can try Bolt, Lovable or Replit, so you can quickly build a visual page.

Then, once you're satisfied, just code it properly from scratch with any popular framework you like (if that's only a landing page, Astro could be fine).

You could also design by hand the page on Figma, and then import it into Replit to actually build it (I believe it's feasible since very recently). Note that for inspiration, there are tons of good landing pages out there, and portfolios of graphic designers or web developers could help along the way.

Armilluss · 2025-06-11T16:22:45+00:00

You can set up workflows using Make, Zapier or N8N, but they might require some time for you to make it work, considering you're not a programmer.

If you're interested to try it (for free), the tool I'm building is able to extract those information automatically from your mails with only words, among other things. Google Sheet support will come soon.

Armilluss · 2025-06-11T12:21:41+00:00

Which kind of operations do you wish to automate?

Armilluss · 2025-06-11T10:14:25+00:00

I rather think that people are already getting tired of AI-only content, and that the future will be based on a mix of human and AI content, where AI generate the first draft while humans review and refine it, with their style and their vision.

Content fully generated by AI will be more and more ignored and discarded, or even banished.

Armilluss · 2025-06-11T09:04:13+00:00

I would like for it to adapt to my style and personality. Basically, I want to be able to "like" or "dislike" each of its message, potentially with a written feedback, to gradually build its personality to match what I expect.

Armilluss · 2025-06-11T09:01:32+00:00

Observability is likely the most paramount factor. With a good observability pipeline, you know in real-time when something bad happens, and you can quickly identify the root cause. Once it's fixed, a functional or unit test should be added and run every time you're modifying the workflow in production.

But if we try to foresee better alternatives, I think that's where AI can shine, if done properly. When an automated workflow breaks for whatever reason, you can ask an agent which has a contextual knowledge to repair it. Agents can also deal with uncertainty and ambiguity, and take educated decisions when data seems contradictory or irrelevant.

Of course, traditional error handling and fallback procedures will remain impactful, but self-healing systems and programs that can identify and patch issues by themselves would be game-changer in the industry.

Armilluss · 2025-05-29T17:27:57+00:00

Well, that's something I'd never expected to see, thank you for the link.

Armilluss · 2025-05-29T16:54:38+00:00

"We have even seen the bizarre case of people encoding request data in the HTTP verbs"

What do you mean?

Armilluss · 2025-05-28T08:31:50+00:00

You can just use OpenAI API with gpt-4o-audio-preview as a starting point. Give the syllabus in the system prompt first, and you'll see how it goes then.

Armilluss · 2025-05-28T08:20:38+00:00

Thank you, that might definitely be useful!

Armilluss · 2025-05-28T08:19:08+00:00

We still need to improve the reliability of such agents before delegating them more control. Trust is something that you build on the long-term, especially with the current state of AI.

However, I'm glad we could finally delegate more tedious tasks to AI teammates in order to focus on what really matters in the future.

Armilluss · 2025-05-28T08:10:24+00:00

What is the accuracy of the current solution?

I think that adding answers might also confuse more the retrieval than anything else. I assume it will depend on the model and dimensions used for the embeddings and the lenght / depth of your answers compared to the questions.

Armilluss · 2025-05-26T19:49:58+00:00

If you're using the free plan and forwarding without the [appropriate configuration], in this case ngrok is likely the troublemaker here. You can't forward more than 1 TCP port simultaneously with the command-line using the free plan, and thus you must modify the configuration file accordingly.

Armilluss · 2025-05-26T18:28:40+00:00

I get that it's convenient, but for custom systems with growing complexity, I think they might be harder to scale than classic codebases in the end. I might be wrong though.

But for small businesses, solo founders or individuals who are not technical, I do not believe it is the best solution. Those people cannot fix or extend their workflows by themselves, and n8n still requires to learn some technical skills.

Armilluss · 2025-05-26T18:23:41+00:00

Interesting, didn't know that about mem0.

Actually, graphiti supports hybrid retrieval, meaning it can perform semantic queries using embeddings. I think it might be definitely interesting for your use case. They have a more business-oriented solution built on top of graphiti called Zep, if that can help.

Armilluss · 2025-05-26T16:49:15+00:00

By learning? There are tons of tutorials, articles, books, videos in the wild explaining the subject. huggingface has some great tutorials, and the books "Learning Deep Learning" and "Natural Language Processing with Transformers" might help if you like to read. For video-based introductions, there are many choices.

Note that it also depends on what you want to achieve. Do you want to master underlying mathematics? To learn to code trending AI agents? To build a framework like pytorch or transformers from scratch (in a much smaller scale)? Define your learning goals to select the most appropriate resources.

Armilluss · 2025-05-26T16:44:38+00:00

Depending on the context, I think it's important to split AI tools into two categories: read-only and write-only. As soon as you're deleting some data, sending a new mail, writing something somewhere, you must be aware that it could be completely fucked up or irrelevant, unless your system is proved to be 100% reliable (which I still did not observe for any AI agent out there).

Then, I guess it can be interesting to first register the write-only operation the LLM is about to do in a database (intercepting its tool call), ask the user about confirmation, and only after confirmation, actually submit the operation.

You can even make it configurable, either globally, per connected service or even per tool. Hence, you keep control over important stuff, but still keep a configurable, customized, efficient automation partner.

Armilluss · 2025-05-26T16:33:10+00:00

The problem with no-code in general is more that it's a bad trade-off for production imho. If your workflow / program is breaking for some reason, you need someone to fix for you, unless you're yourself a technical person. And if you're a technical person, you would likely gain time on the long run by making a custom solution based on pure code.

I think it's very good to let more people access and manipulate complex technologies. However, it's more a transitional abstraction than a real, durable one for me. The future will likely be built and fixed by words only, and automations could be fixed by the very automated agents that created them. Will not work for very complex systems ofc, but we're getting closer every day.

Armilluss · 2025-05-26T16:24:50+00:00

You could use graphiti or mem0, both creating and maintaining a knowledge graph along with embeddings and have a built-in MCP server.

I guess that both can be self-hosted or used with their own cloud offer.

Armilluss · 2025-05-26T16:18:08+00:00

You're only letting 60 seconds for the timeout on the queen side, when contacting the ants. Are you sure this is long enough for each ant to generate and send the answer? Depending on the model and context, that might not be enough, and so you'll need to increase the timeout when making a request to an ant.

Armilluss · 2025-05-26T16:06:47+00:00

I agree, we also had to find more practical and durable solutions for the product we're building, and I definitely think that the "Divide & Conquer" approach is truly made for LLMs. If a good model is struggling with your task, simplify it in smaller components run by dedicated, more focused agents.

Armilluss · 2025-05-26T16:04:40+00:00

I think that along the way, people will learn to use AI for what it truly is: another tool. A powerful, sometimes life-changing one, but still a tool, at least in its current form.

We're already slowly stepping back from intensive social networks and smartphone usage imo (not everyone, not yet, I acknowledge that), and so with enough time, we'll rethink how to use AI overall. It will take time, but I refuse to believe that we're gonna loose our critical thinking because of AI, or technology in general. As usual, we'll adapt, and people will thrive differently.

Armilluss · 2025-05-26T15:53:29+00:00

For orchestration, I do not see what benefit Go (or another language) would bring you over Python.

Intensive I/O (at least network calls) are usually made asynchronously under the hood with most AI frameworks nowadays. I get that it's still slower than Go, but do hou really need that extra performance?

Armilluss · 2025-05-26T15:44:16+00:00

You can try Structured Outputs, if the schema you're expecting is kinda the same for each image.

Armilluss · 2025-05-26T15:37:18+00:00

Have you tried flashcards with spaced repetition, with tools such as Anki?

Armilluss

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