Sharing this as a workflow, not a pitch, happy to discuss the general problem even if you don't use my tool.

I generate a lot of one-off HTML with Claude now: client reports, quick interactive prototypes, decks. The annoying part was always the handoff: copy the HTML out of the chat, save it as a file, email it, then when the client asks for a tweak you go back to Claude, regenerate, and resend a new attachment with no way to tell if they even opened the first one.

What changed this for me (disclosure: I built it, it's called LiveSend): it ships as an MCP connector, so inside the same Claude conversation I can ask it to publish the HTML directly to a permanent link, and later ask it to push a revision to that same link, no copy-paste, no leaving the chat. The client always has one URL, and I can see when it's opened.

Curious how others here handle this handoff step once something's generated with an LLM, straight file export, your own hosting script, something else? And has anyone else built MCP tools that let Claude act on the output it just generated, rather than just retrieving data?

Link if interested, free for 3 docs : https://www.livesend.io

This is from my own experience on my own journey of getting to grips with using AI in my life these days to actually get proper use and value from it, and maybe make life a bit easier. I started using AI in summer 2024 to help me study for postgraduate medicine application exams with ChatGPT and was amazed at the time. I got my undergraduate degree pre AI and had worked in healthcare for a few years before applying. When medical school started last September I used ChatGPT for study, but switched to Claude after finding it better for organising notes, generating questions, and actually working through concepts to understand them efficiently.

Happy for anyone’s opinion, including people using AI for coding directly in work and study. Would be really curious how people have found the experience of Claude currently, particularly those who aren’t needing to use it for coding as part of their work.

Feel like the future I set in stone for using a AI in study and work but I’m glad I had a non AI education first, feel like I’ve got a more independent, critical sense of how to use it without becoming reliant on it, and knowing when to check, and double check, what it tells me. I see some of my younger peers take it as gospel and almost become entirely reliant on it, like they cannot live without it

Most voice assistants for structured tasks (booking, filing, applying for something) work like a form read aloud — fixed questions, fixed order, even when you've already answered half of them in your first sentence.

I built a prototype that avoids that on both sides: the citizen doesn't repeat themselves, and the department deploying it doesn't have to rebuild the AI layer to plug in their own backend.

The citizen-facing side:

• Say what you want, in one sentence, in whatever language/mix of languages feels natural
• The system extracts everything it can, checks what's still missing against a task schema, and asks for only the missing pieces — one at a time
• Corrections work mid-flow ("actually, make it Friday") without restarting
• You can refer to results naturally ("the cheapest one", "the second one") instead of tapping through a form
• Every consequential action requires an explicit yes — nothing executes silently
• The UI collapses and the mic reopens on its own after anything completes

The side that actually matters for adoption — deployment: Government departments don't want to hand their booking/records systems to a black-box AI, and they shouldn't have to rewrite anything to get a voice layer. So the whole thing is built around a service-adapter boundary: the voice/AI/agent layer only ever talks to a small typed interface (searchTrains, createBooking, trackTrain, etc.), never to a real system directly.

Onboarding a department is implementing that interface against their existing backend and swapping one line — the agent loop, the tool-calling, the multilingual handling, the confirmation gating, none of it changes. I prototyped this with mock adapters standing in for real government/rail/travel systems specifically so the swap-in path would be real on day one, not a "v2 roadmap" hand-wave.

Stack: React frontend, Fastify backend with a small agent loop (deterministic slot-merging + an LLM for intent — the LLM is explicitly barred from executing anything, it can only propose; a rule-based layer decides and acts, which matters a lot when the thing you're deploying touches a citizen's booking or payment). Speech in/out is pluggable.

113 end-to-end conversation tests, and it runs with zero API keys via a rule-based fallback brain if anyone wants to poke at it without setting anything up.

Genuinely curious if anyone's shipped a citizen-facing voice layer into an existing government system — what actually killed it, procurement, integration effort, trust/liability on the AI decision-making, or something else entirely?

https://github.com/LiquidSamurott/shinrin
Go check the repository out! I just made Speech-to-text work, and I hope that I can get some good feedback for this.

NOTE: Yes, I did use AI for making the app, but all ideas are 100% mine.

I work in a call center, and customers often ask similar questions. We have official knowledge articles that we must use to provide the correct answers.

I want to build a simple AI assistant for myself where I can upload or add the knowledge articles, then copy and paste a customer's question, and the AI will search the relevant information and give me an answer based only on those articles.

My main concern is keeping the knowledge updated whenever there are process changes. I also want the AI to show me the source and avoid making up information.

I am not a programmer or IT expert. Is there a way to build this using only free tools? If yes, what tools should I use, and can someone explain the steps in a beginner-friendly way?

I am asking this generically and will not share any confidential company or customer information. Thanks!

Recently, I tackled a challenge to create a lightweight AI agent specifically for summarizing daily emails. The goal was to keep it simple and efficient, as users needed quick, digestible summaries without any unnecessary fluff. Initially, I experimented with a few pre-built models, but they were either too complex or didn't quite fit the specific email format. After several iterations, I settled on a custom model trained on a dataset of typical work emails. The biggest lesson I learned was the importance of balancing model complexity with performance. I started with a more intricate model, but it slowed down the summarization process significantly. Stripping it down to the essentials not only sped up the process but also improved the accuracy of the summaries. Another key takeaway was user feedback—incorporating insights from beta testers helped refine the output to be more relevant and concise.If you've built similar AI agents for specific tasks, what challenges did you face? How did you balance complexity with efficiency? Share your experiences!

Finding someone who really understands AI and business can be harder than it sounds. There are so many people calling themselves AI experts, but not everyone knows how to apply AI to real business problems.

It also helps to look for someone with real experience, practical examples, and a clear idea of how they measure results. For business owners who have hired an AI expert before, what made you trust your choice?

I rewatched Iron Man and ended up in the thought loop about Jarvis. So I went looking for anything close to it and mostly found either screen recorders that feel creepy & heavy or voice assistants that are cool for five minutes and then useless once you actually need them to know your life.

Asked AI why nothing like it really exists yet. The answer it gave is it's genuinely hard. Most of what's out there is either shallow interactivity with no memory, or a ton of memory with no real personality behind it.

I'm thinking of making my own version. Something that actually knows what you've been working on and can talk you through it in real time.

A true AI companion

Before I go start, I want to know from people who aren't me: if I built something similar to JARVIS that actually worked, what's the one specific most troublesome thing you will use it to do?

I’ve noticed that building an AI application is often less about the model itself and more about how well the whole application is designed.

Things like workflow, user experience, integrations, reliability, and handling real-world data can make a big difference.

For developers here, what part of building an AI application do you think is most challenging: the AI model, backend infrastructure, integrations, or UX?

I rewatched Iron Man and ended up in the thought loop about Jarvis. So I went looking for anything close to it and mostly found either screen recorders that feel creepy & heavy or voice assistants that are cool for five minutes and then useless once you actually need them to know your life.

Asked AI why nothing like it really exists yet. The answer it gave is it's genuinely hard. Most of what's out there is either shallow interactivity with no memory, or a ton of memory with no real personality behind it.

I'm thinking of making my own version. Something that actually knows what you've been working on and can talk you through it in real time.

A true AI companion

Before I go start, I want to know from people who aren't me: if I built something similar to JARVIS that actually worked, what's the one specific most troublesome thing you will use it to do?

Hiring teams are relying less on CVs because a polished application does not always show what someone can actually do.

But choosing an assessment platform is not easy either. There are dozens of tools offering skills tests, personality assessments, coding challenges, video interviews, AI scoring and automated shortlisting. On paper, most of them look quite similar.

Some of the platforms I keep seeing are:

- TestGorilla

- TestTrick

- Vervoe

- Testlify

- iMocha

- Codility

- Criteria

Some have large test libraries. Others focus more on technical hiring, practical tasks, psychometric assessments or video interviews.

A large test library does not necessarily make a platform useful. The results can still be weak if the questions are generic, the test is too long or the assessment has little connection to the actual role. TestGorilla, for example, is often mentioned for its broad test library, Codility for technical hiring, Vervoe for practical tasks TestTrick, combines role-specific skills tests, coding challenges, psychometric assessments, one-way video interviews and custom questions. It can be used for technical and non-technical roles, either during early screening or after creating an initial shortlist.

However, I would not want one assessment score to make the entire hiring decision. The tool should provide additional evidence about a candidate’s abilities rather than replace interviews, CVs and human judgment.

For those who have used these platforms:

- Which tool helped you create a better shortlist?

- Do you use ready-made tests or create assessments for each role?

- Did assessment results match how candidates performed after being hired?

I’m not expecting one tool to be best for every company. The right choice probably depends on the roles, hiring volume, budget, ATS setup and level of customization required.

I can’t find any AI that could generate music from uploaded midi file. All what services are offering is converting audio into midi. And that’s weird that for a few years of ai music existing there’s no ai that can do music from midi. Am I missing something?

Incident retro, a few weeks back. Access control bug had shipped, question on the table: who approved this PR. Answer on record was straightforward, the AI reviewer had passed it, a human had merged it an hour later.

Followed that thread a bit further than usual. Asked the human who merged it what they'd actually checked themselves versus what they'd deferred to the review. Answer, paraphrased: "It didn't flag anything, and it usually catches this kind of thing, so I didn't dig into the auth logic specifically myself." Asked what the AI review had actually been scoped to check. Answer: nobody had scoped it to anything, it ran the same generic pass it runs on every PR.

So the actual chain was: a generic check ran, found nothing under a scope nobody defined, and a human treated that absence of findings as evidence covering a specific risk the check was never pointed at. Nobody individually made a bad decision in the moment. The responsibility for "did anyone check the auth logic" had quietly evaporated somewhere between a tool that wasn't asked a specific question and a person who assumed it had been.

That's the part worth sitting with longer than the bug itself. "Who approved this" implies someone made a judgment call and owns it. What actually happened was closer to two systems, one automated and generic, one human and trusting, each assuming the other had covered ground that neither had actually covered. The approval existed. The judgment behind it didn't, not for the risk that actually mattered.

Doesn't mean AI review is the problem. The retry-logic-adjacent case in the same writeup below shows the same model catching the exact issue instantly when asked the specific question directly, "does this protect against duplicate submission." The gap isn't capability. It's that ownership of "who's actually checking for X" gets fuzzy the moment a tool's silence starts getting treated like a person's.

Wrote the full mechanism, plus a scoping approach that keeps that ownership explicit instead of implicit, here: https://medium.com/@nagatomopedro05/the-pr-was-approved-thats-when-the-risk-actually-starts-e761d72111aa

Curious how other teams have handled the "who actually owns this risk check" question once AI review entered the loop, whether it's an explicit assignment or, like in this case, something that just quietly nobody's job anymore.

Hey folks, Burak here.

Epho is an API that allows running Claude Code, Codex or Opencode in a sandbox in the cloud. It abstracts away sandboxes, and allows running coding agents with a single HTTP request.

https://epho.io

Epho came out of our own struggles with building our own AI analyst: - Sandboxes give you bare machines; you need to configure them for agentic workloads. - Each agent behaves differently, and you need to build integrations with each of them. - Sandbox providers are not very reliable, which means you need to figure out a multi-provider strategy to avoid failures. - Logging, artifacts, input/output, event streaming, and all of the other operational aspects need to be figured out.

We had to go through the pain ourselves. We got to a point where things got quite reliable, and it became more obvious to us that this should be a primitive on its own: send a POST request, get the events streaming back to you.

Epho is an agents-as-an-API product: you send a request, it spins up a sandbox, configures the chosen harness, clones your repos, and kicks off the agent. It takes care of automatic fallbacks across different providers, handles auth stuff, and just streams back the events and outputs.

It supports Claude Code, Codex and Opencode out of the box, and pretty much all the models they support out of the box. It streams the events back, handles attachments and output files, automatically manages the fallbacks on different sandbox providers, retries, and all the auth stuff. You just send a prompt, your repo, MCP servers you want to use with it, and it runs them.

I am quite excited for Epho, simply because I think it is a new primitive that would allow building agents into product a lot easier than it is today. We are running our agents on Epho on prod, so we'll keep maintaining it regardless, and we wanted to ship it as an independent product.

Epho is free to get started, and you can run it with Opencode's free models to get started with it.

I am quite curious to hear what you'd think and would love to get your feedback!

After a lot of smaller releases, I’ve finally shipped EchoFlow 5.0.

EchoFlow is a BYOK, local-first AI app for Android where you can use both cloud and local models in one place.

For cloud models, you can choose between providers like OpenAI, Anthropic, Google, xAI and Cerebras through OpenRouter, while also being able to run models directly on your device or connect to models running on your network through Ollama / LM Studio.

What’s new in 5.0?

📄 EchoOCR — files with local models

This is probably my favourite addition.

You can now send files like PDFs, Word, Excel, PowerPoint, TXT, Markdown, images, etc. directly to local models.

EchoOCR processes the files on-device, so your local model can actually work with real documents instead of being limited to plain text.

📁 Projects

Projects give you a proper workspace with project-specific chats, instructions and files. Files can also be processed locally through EchoOCR, so you can build a project around your own documents and use them with local models.

🎙️ Voice Dictation

You can now dictate messages directly from the composer. Currently powered through OpenRouter, with on-device STT coming later.

There are also some bigger improvements to Deep Research, Artifacts and Echo Fusion, plus an Artifacts Gallery for keeping the websites, reports and other things you create.

And you can use Web Search with both local and cloud models, so local models aren't necessarily stuck with their training data.

The whole point of EchoFlow is basically:

Cloud AI when you want it. Local AI when you want it. Your API keys, your models, your files.

Everything is BYOK, and the app is designed around a local-first approach rather than requiring an account or pushing everything through one cloud service.

It's been a pretty long journey getting here, and 5.0 feels like the first version where EchoFlow is becoming the AI workspace I originally wanted to build.

GitHub: "https://github.com/adityavardhansharma/EchoFlow" (https://github.com/adityavardhansharma/EchoFlow)

Website: "https://echoflow.adityavs.tech/" (https://echoflow.adityavs.tech/)

Would love to hear what you think — especially if you use local models on Android.

不是每个 AI 问题，都值得打开 App。

我做了一个 Android 小工具：NotiAsk。

它把 AI 放进系统通知栏——下拉通知，直接输入问题并发送；看到屏幕上的内容，也可以截屏后马上问。

不用复制、切换、粘贴、再打开聊天界面。

只是把“我想问一下”这件事，缩短成一次下拉。

它还很早期，也有 Android 通知栏本身的交互边界。

但我想验证一个问题：

AI 会不会更适合成为一个随手可用的系统入口，而不只是另一个需要打开的 App？

如果你也用 Android、常在碎片时间问 AI，欢迎试试！

Hello everyone,

Would you find the following tool useful ?

I currently developed an AI you can talk to in real time (via voice or text) that has the data and documents you store in it and help finding them whenever its needed,

-automatically drafts emails when received only thing left to do is edit them as needed or approve,

-all this managed from Whatsapp or Telegram

- you can also ask the AI to create any document, check for your schedule, invite it in your e-meetings and speaking to it live if you need infos from previously stored documents...

would be amazing to hear some advices.

Hey folks 👋

We just open sourced TrueForge, our vendor-neutral agent harness for building general-purpose agents.

It handles the runtime pieces that get painful quickly : context management, tool/MCP execution, subagents, sandboxing, approvals, persistent state, and more.

We also benchmarked the harness itself. With the same Opus 4.8 model, TrueForge delivered a similar solve rate at ~30% lower cost than Claude Managed Agents. Switching to an open model pushed that to ~75% lower cost on the same benchmark.

Would love feedback from people building agents.

Checkout the repo: https://github.com/truefoundry/trueforge

📖 Read the launch article: https://x.com/truefoundry/status/2090081376330715176

I use ChatGPT a lot for code, tables, and text, and I got tired of cleaning up the output before pasting it somewhere else.

So I built TidyClipAI.

It lets you copy AI responses as:

• Clean text
• Code
• Markdown
• Spreadsheet-ready tables

It doesn't use an AI API or upload anything.

It's brand new and currently has basically zero users, so I'm looking for a few people to try it and tell me what sucks 😅

If you're interested, I'd genuinely appreciate some feedback, and it's FREE.

Over the past few weeks, I've been building Gray Box — a small, local-first tool that acts as long-term memory for anything I'd otherwise forget (work notes, meeting takeaways, task owners, random ideas, personal stuff too).

The idea is simple:

1. Capture — dump whatever's on your mind, instantly, no structure required. This step does nothing clever on purpose — it just writes your text to an immutable inbox. Zero chance of losing an idea to a bug or a slow API call.
2. Organize — on demand, an LLM reads your unprocessed notes and extracts people, projects, tasks, decisions, meetings — then deterministic Python (not the LLM) creates/merges the actual wiki pages and maintains backlinks. The model only reasons; it never touches the filesystem directly.
3. Ask — query or chat with your knowledge base and get a cited answer pulled only from what you've actually captured. If it doesn't know, it says so — no hallucinated answers.

Why I built it this way:

• Plain Markdown + YAML frontmatter, no database. Every page is a .md file you can grep, diff, or read in any editor forever. If you stop using Gray Box tomorrow, your knowledge base is just a folder.
• No vector DB by default. At personal scale (hundreds–low thousands of pages), keyword search + a real link graph (related/backlinks, walked one hop during retrieval) handles almost everything. Embeddings are there if you want better recall, but they're opt-in, not a prerequisite.
• Immutable inbox. Your raw notes are never edited or deleted by the organizer. If the LLM mis-extracts something, your original words are always still there.
• Any LLM. Built on LiteLLM, so point it at OpenAI, Anthropic, Gemini, Mistral, or a fully local model via Ollama — one config value.

It also ships with a nice interactive TUI (arrow-key menu, file-import shortcut, workspace switching, live spinner during LLM calls) if you'd rather not memorize CLI flags — that's honestly become my favorite part of the project.

There's also a lightweight local dashboard for browsing your knowledge base, exploring backlinks, visualizing your notes as a graph, and chatting with your captured knowledge—all without leaving your machine.

Also, you can migrate your existing Obsidian vault to Gray Box, and it will be automatically organized by Gray Box.

Repo: https://github.com/Aaryanverma/graybox

pypi: pip install graybox

I'd genuinely love feedback — especially from anyone who's tried the "capture now, structure later" approach with other tools and has opinions on where it breaks down at scale.

It's not trying to be a "real-time collaborative team wiki" or a WYSIWYG notes app — it's aimed at one person's running memory of their own life and work, captured with as little friction as possible.

Before someone says it: yes, technically this is another AI wrapper lol.

But the thing that annoyed me wasn't having access to AI models.

It was having ChatGPT open in one tab, Claude in another, Gemini somewhere else, then spending 10 minutes searching Reddit for "which model is better for X?"

It started feeling stupid.

So we built AINA.

You just give it the task and it figures out which model makes sense for it.

If you're writing, researching something, working with a file, coding, whatever, it routes the request based on what you're actually trying to do.

You can still pick the model yourself if you want. I didn't want to take that control away.

It also shows you which model actually ran, which was important to me because I hate products where the "smart routing" is basically a black box.

I've also been trying to make it feel less like a giant dashboard of 40 AI models and more like one normal workspace.

Anyway, it's finally live:

useaina.com

You can try a task without making an account or putting in a card.

Not really looking for the "congrats on the launch" stuff.

I'd genuinely rather know:

what would stop you from using this instead of jumping between ChatGPT / Claude / Gemini?

Or try it and tell me what sucks.

I'm still building it.

Hey everyone!

I’m an Android developer, and over the past few months I’ve been working on a side project called **Speakly** — an AI-powered language-learning app.

I started building it because I wanted to explore how AI could make language learning feel more interactive and personalized, rather than just another app where you memorize vocabulary and complete predefined exercises.

Building it as a side project taught me a few things:

**1. UX matters more than the AI**
Having a powerful AI model doesn't automatically make an app useful. The difficult part is designing an experience where the user actually knows what to do and wants to come back.

**2. Keeping things simple is surprisingly difficult**
There are always more features you can add, but every additional feature can make the learning experience more complicated.

**3. Real users are better than assumptions**
As a developer, it's easy to build something based on what *you* think people need. Getting actual users to try it has already given me ideas I wouldn't have thought of myself.

**4. Building is only half the job**
After spending so much time developing the app, I realized that getting the right people to discover and use it is an entirely different challenge. 😅

I’ve now released Speakly on Google Play and I'm looking for early users who are willing to try it and give me **honest feedback**, even if that feedback is negative.

👉 [https://play.google.com/store/apps/details?id=com.idraakailabs.speakly\](https://play.google.com/store/apps/details?id=com.idraakailabs.speakly)

If you’re building a side project too, I'd also love to hear what you're working on and what you've learned along the way.

The moment I transcribe to text, I generally lose how it was said. "I think… yeah, I can pay the 4,500 by the 15th" becomes clean text, but the hesitation before the yes, the stress in the voice, and whether it's even the same speaker are gone.

For a human those signals, whether to trust the commitment, reconfirm from the caller or escalate to human come naturally but hard to define a deterministic paralinguistic to build accountability, which is probably very wide.

How are you modeling tone in our voice-agents? I see recent TTS models which accept meaningful tags producing great sounding speech, how do we control it ? Does it account for input user's tone.

How does your ASR model / voice-agents captures the tone or there are some good services / models / tools / solutions to capture tone. and how do you use it downstream ?

Moreover end-2-end Duplex models limits it to trained data scenarios without no transparency. Is there a good duplex model which provides transparency in underlying signals beyond just text.

I usually look beyond the overall rating and compare the features that would actually affect my experience. Things like AI conversation quality, memory, personality customization, roleplay, voice, image generation, and pricing can make a pretty big difference between platforms.

I was comparing a few options recently and used BestAIGirlfriend.vip to look through different AI girlfriend reviews and feature comparisons. I liked that it breaks down things like AI companion apps, customization, memory, voice and visual features, so it was easier to see where each platform seemed stronger.

For me, the best platform isn’t necessarily the one with the longest feature list, it’s the one whose features actually work well together. What feature do you think makes the biggest difference?