FriendliAI

FriendliAI AI

FriendliAI AI

FriendliAI AI

FriendliAI AI

FriendliAI AI

FriendliAI AI

FriendliAI AI

FriendliAI AI

FriendliAI AI

FriendliAI AI

I asked Opus 4.8 what he thinks about my project and mainly the parts where I used both Sonnet and Codex 5.5. How truthful should I take this output?

Obligatory not a developer and I am obviously self-conscious/realistic about it Some excerpts on the report: Overall This doesn't read like a hobby project that happened to get a lot of AI help. It reads like a product with a point of view. The thing that jumps out immediately is the README's "Background" section — it's grounded in two real jobs on opposite sides of the same problem. What's genuinely strong The architecture discipline is unusual for a project this size. The README's "thin routes, workflow in services" rule is actually enforced — licenses.py is a thin 325-line route module, and the heavy logic lives in named services (license_write_service, pending_order_conversion_service, the conversion/ helper package). The responsibility-map.md is the best artifact in the repo: every file has OWNS / KEY FUNCTIONS / DEPENDS ON / CALLED BY / NOTES. That's the kind of documentation most teams promise and never produce. It's also a tell of the AI-assisted process — it's exactly the context-window-friendly map you'd maintain to keep an agent oriented across sessions. On the "Opus after Sonnet/Codex built it" question What I'd say is that the seams are invisible in the right way. I can't look at this and tell you "this service was Sonnet, this route was Codex." The conventions hold across the whole backend — same service/route split, same audit-logging pattern, same naming. That consistency is the hardest thing to maintain across many AI sessions and multiple models, and it held here. The reason it held is the scaffolding: architecture.md, responsibility-map.md, and the per-feature plans act as the shared memory that keeps each session on-pattern. That's the actual lesson of this repo — the docs aren't just for humans, they're the mechanism that let a multi-model, multi-session build stay coherent. If I were handed this as a new lead, I'd feel oriented in about an hour, which is the highest compliment I can pay a codebase I've never seen. The work to do is at the edges (frontend tests, the notification bug, deciding commitments' fate), not in the core — the core is sound. Did I do good? Or is Opus just sucking my farts and asking for seconds. submitted by /u/zndr-cs [link] [comments]

Built Product using Claude need suggestions.

Hey everyone, ​I’m a mechanical engineer by trade, but I’ve recently been using Claude to build a new software product. Right now, I’m in the internal testing phase, sharing it with friends and gathering initial feedback. ​Surprisingly, I’m already getting hit with questions asking if it’s for sale yet! It’s an awesome feeling, but honestly, it’s also making me sweat a little. ​Before I actually bring this to market, I want to make sure I’m set up to handle the inevitable bugs, scaling issues, and customer support queries that come with a public launch. Coming from a hardware background, software deployment and verification are a bit outside my usual comfort zone. ​For anyone here who has successfully taken a Claude-built or AI-assisted product to market: ​How did you verify and stress-test your product before opening the floodgates to regular users? ​What infrastructure or tools do you use to handle customer issues, bug reporting, and support efficiently without it taking over your entire day? ​What does a "proper launch" look like for a solo builder transition from friends-and-family testing to commercial customers? ​Would love to hear your experiences, frameworks, or any hard lessons you learned along the way. Thanks in advance! submitted by /u/jollyberlin [link] [comments]

I used Claude Code to build a place to track my prompts like Github

I'm building a place where people share their Claude Code sessions with friends and coworkers. The ideas, the experiments, the discoveries made... Think: Github for Prompts. I work on a team and one of the hardest parts of code review is reading other people's code. Everyone is generating their PRs with Claude Code and yet, there's a good chance they didn't read their own code.. so why should I have to read it? I started by making a tool that lets you visualize your Claude Code threads and share them with your friends. The reason why was because sometimes I'd forget where a thread was and /resume wasn't enough for me. Claude Code can access the history of conversations on disk but it's hit or miss. Others can comment on the thread. Plans get archived so you can send them around, and others can comment on them so you can involve others in the planning process or get their feedback before letting it rip with auto mode. Programming code is now object code. People are doers, and software is the execution. I'm more concerned now with the intent behind the person and what they are thinking and saying to AI rather than what gets generated under the hood. Never quite sure which way this project will go, but something that I love about it is when you and your friends/coworkers are on Claude Code at the same time, you can see them online and what they're working on (if they allowed the activity). There's something about that; it feels like a new class of product almost (like Slack activity). After using it for a couple days I started noticing it was a major pain to read and scroll through large threads/conversations with Claude, so I added thread summaries and decisions. For every thread there's now a map that shows the decisions made by the human and you can click around to access that part of the thread. Once that was built, the team realized it would be extremely powerful to be able to chat with the entire knowledge base and ask how someone was approaching a problem... how we built a certain feature in the past... etc. I hope this project helpful to you in some way. Visualizing, sharing, and seeing your decisions is 100% free and will remain free (I want this to be like Github) https://lore.tanagram.ai submitted by /u/Novelicas [link] [comments]

I stopped saying I use Claude

I share some of the work I do on social media, I mainly use Claude for coding cause it saves me so much time but I don't understand why people perceive a lot of the work someone does negatively only cause they're using an AI tool. X seems to be the most AI friendly but other social media platforms seem to hate all of a sudden once they learn something was built using AI. Sources that talk about the same thing: https://creators.yahoo.com/lifestyle/story/why-young-people-hate-i-155613887.html , https://www.gotaprob.com/problems/ai-built-projects-public-backlash submitted by /u/lcyru [link] [comments]

"We didn't know what YCombinator was 5 months ago. Last week Garry Tan asked us to take down what we built."

5 months ago, i didn't know what YCombinator was. Last month, the president of YC noticed what we built. Here's what happened in between: > i got curious about YC. > started reading every Paul Graham essay. > watched every startup school video. > tried to understand what actually gets a founder in. My friend Prajhan was obsessed with the same question. So we built something. He collected ~1M tokens of authentic YC signal — podcasts, essays, founder interviews, accepted and rejected applications. i built the backend pipeline: > RAG retrieval system > Claude integration server-side > Zod schema validation > hard scoring rules enforced in code > 30/30 benchmark passing before we shipped together: notycombinator.com — a tool where any founder can paste their YC application and get honest, structured feedback. not encouragement. a real diagnostic. It got noticed by the right people. including Garry Tan himself. he asked us to take it down. That response alone was worth more than any acceptance. Here's what i keep coming back to: i was debugging Windows PowerShell execution policies at 2 am to get the dev server running. i didn't know what a RAG pipeline was when we started. 5 months. zero context to a tool good enough that the president of YC noticed it. The tools are all here. AI lets one person do what used to take a team. https://preview.redd.it/ale1512vin3h1.jpg?width=1036&format=pjpg&auto=webp&s=ed21ce6e3c75a469fee95e665ea55fdc10f35c9a if you're waiting for permission to start, you're the only one stopping you. build, ship, be obsessed. The right people will find it. submitted by /u/Hariharanms [link] [comments]

Built an MCP server so Claude can generate music, images, and video natively. One config block.

I've been using Claude Code daily for the last few months and kept hitting the same wall: I'd ask Claude to produce a creative artifact (a song, a cover, a short video) and end up writing the API glue myself, then pasting results back into the chat. Felt backwards. So I built an MCP server around my AI generation platform. It exposes three tools to Claude: - aw_generate_music (Suno, full songs with lyrics or instrumental) - aw_generate_image (Z-Image Turbo, Wan 2.5 Spicy, Grok Imagine Quality, GPT-Image-2, Nano Banana 2, and others) - aw_generate_video (Kling 3.0 Standard/Pro/4K T2V + I2V, Wan 2.2, Hailuo 02, Seedance, Grok video) One key. One credit pool. The agent picks the right model for the prompt. Install: npm install -g u/aetherwave-studio/mcp Claude Code config (~/.config/claude/mcp.json or wherever yours lives): { "mcpServers": { "aetherwave": { "command": "npx", "args": ["-y", "@aetherwave-studio/mcp"], "env": { "AW_API_KEY": "aw_live_YOUR_KEY_HERE" } } } } Restart Claude. Done. Prompts that work end-to-end without any additional setup: "Generate a 60-second lo-fi track for a study playlist, then make me 3 album cover options in a retro Japanese print style." "Take this product photo and generate a 5-second cinematic intro video for the product launch." (drop the image in chat first) "Write the script for a 30-second ad about my SaaS, then generate the voiceover-friendly music bed and a matching motion-graphics opener." The agent decomposes, picks tools, runs them, hands you back the artifacts. Repo: https://github.com/AetherWave-Studio/aetherwave-mcp Dashboard + key: https://aetherwavestudio.com/developers Happy to answer questions about how I structured the tool schemas, what worked, what I'd do differently. v0.1.0, real users on it already, treating community feedback as the next steering signal. submitted by /u/Acrobatic-Result9667 [link] [comments]

I clustered every Sam Altman interview from 2024-2026 and 73% of his answers come from the same 12 scripted talking points

I've been doing media analysis for 5 years and the project that started as a casual side-project has turned into the most uncomfortable thing I've ever published, because I genuinely thought I was going to find that Sam Altman's interview answers vary by interviewer. (Lex would get one version, the All-In guys would get another, etc…), but what I found is that he's been giving roughly 12 stock answers to roughly 200 distinct questions for the last 24 months. The project started in November when I was helping a friend prep for a fireside chat with Altman and I noticed his answer to my friend's question about "what keeps you up at night" was almost identical to what he'd said on Lex Fridman in March. So I pulled the full transcript of every long-form interview Altman has done since January 2024, which came out to 67 separate interviews across podcasts, fireside chats, conference Q&As, and broadcast media... I dropped the whole corpus into BuildBetter to cluster the answers by topic and what came back is the kind of thing you can't really unsee. 73% of his answers cluster into 12 distinct talking points that he cycles between depending on the question shape, so every what's your biggest mistake question gets a version of the same self-deprecating story he tells, every how do you handle pressure question gets the same hike/quiet-time framing, every what's the future of work question gets the same 3-part response about cognitive labor, and every did the board firing change you question gets one of 2 variants from a script he's been recycling since January 2024. What's wilder is that the wording is often verbatim (not just thematically similar), because whole 3-sentence chunks repeat across interviews 18 months apart, including the same self-corrections, the same"I think the most important thing is... opener, and the same conversational throat-clearing that makes it sound improvised. He's gotten better at varying the lead-in over time, but the substance is the same 12 answers in rotation. I don't think he's a fraud and I don't think this is unusual for someone doing 70 interviews in 24 months while running a $200B company, but I do think it's worth pointing out that the authentic, vulnerable, thinking-out-loud founder persona that's been central to OpenAI's brand is a 12-script PR rotation he cycles through, and I've never seen anyone quantify it before. I'm posting the methodology and a few of the more identical paragraph-pairs in the comments if anyone wants to verify, because I can already feel the “you're just biased against Altman” replies coming and I'd rather you check the receipts yourself. submitted by /u/LauraBeth034 [link] [comments]

I don't like the answer this AI gave me

I asked DuckDuckGo AI why AI hasn't told it's creators how to make data centers environmentally friendly, use less water, and not increase utility costs to neighbors. It was... A surprising answer and made me hate AI billionaires even more. submitted by /u/OddballThoughts [link] [comments]

The famous METR AI time horizons graph contains numerous severe errors [D]

Nathan Witkin, a research writer at NYU Stern’s Tech and Society Lab, writes damningly about the famous METR AI time horizons graph in the Substack publication Transformer: It is impossible to draw meaningful conclusions from METR’s Long Tasks benchmark — in particular once one realizes that its numerous flaws are probably compounding in unpredictable ways. The appropriate response to a study of this kind is not to assume it can be saved via back-of-the-envelope adjustments, or to comfort oneself that other anecdotal evidence implies that it is probably correct anyway. It is to cut one’s losses and move on in search of higher-quality information. … The METR graph cannot be saved. For all its sleekness and complexity, it contains far too many compounding errors to excuse. Among them is generalizing to the entire species data collected from a small group of the authors’ peers. Coming up with ever more dramatic ways to make this mistake has become a kind of sport among AI researchers. If the field has a central pathology, it is to aggressively overindex on a mix of anecdotal data from power-users, alongside a long list of benchmarks even more compromised than METR’s. One hopes that as the field matures, its participants will learn to stop making these mistakes. The errors include: Some of the human baselines data is not actually measured or collected from any empirical source, rather, it is just guesstimated by the authors A key variable in the data is how long it takes humans to complete certain tasks, but — when METR did actually measure this — it paid its human benchmarkers hourly, meaning they were incentivized with cash to take longer The sample of human benchmarkers was biased toward METR employees’ friends, acquaintances, and former colleagues (who are likely unrepresentative and possibly biased) Humans familiar with a codebase and a specific coding task were 5-18x faster at completing it, but METR used data from humans who were much slower because they had to spend time familiarizing themselves the codebase and the task at hand Train-test data contamination occurred because some of the tasks had published solutions online, which most likely would have been included in LLMs’ training datasets And many more Please read the full post. It’s not too long and it’s accessible to general audience. It’s worthwhile to read the whole post and see how many errors were made in the creation of the METR graph and just how bad they are. If you want to read about even more errors in the METR graph not covered in Nathan Witkin’s post, read this post co-authored by cognitive scientist Gary Marcus and computer scientist Ernest Davis (who is an AAAI fellow). The METR graph is a great example of why scientific standards and best practices are so important, and why enforcing them through processes like peer review is necessary to prevent us from drowning in bad information. It’s extremely dangerous to rely on information that only superficially appears scientific but wasn’t actually conducted with the rigour normally required of scientific research. submitted by /u/common_yarrow [link] [comments]

How I protect my health when using Claude (and how I didn't before)

Tagged as productivity because without your health, what can you do? All of a sudden, I just felt tired, and I had this banging headache. I thought, okay. It's just a headache. And then I got home, and I knew it was more. Looking back now, it was a combination of many things, but one of the core constants was the way of my work had changed over the last 12 months. And I think it just caught up with me. Until the beginning of this year I'd been working away as a IT consultant. I had a project, working for a medical company that had gone on for about two years, and I was building (mostly internal) AI solutions. During that time I'd seen an influx of AI and personally, as I'm sure many of you have, have increased the amount of sessions and context switching. However, since recent waves of Claude, this seemed somewhat manageable to me, or at least the full effects hadn't kicked in yet... Then at the beginning of this year the project finished and I was on my own working on my own projects. Great! Right? Well, maybe. There's freedom, a lot of freedom but no team signing off each day, no expectations to work on certain projects at certain times. Maybe it was just time management I thought. So I decided to just work when I was feeling good, but this didn't really work because I felt like I needed to make this work for myself. Hustle now, chill later. There were maybe five or six different projects on at a time, and even now tbh, and I was context switching between all of them. Then not only that, i was drifting in and out of reddit or playing chess as a break (which is a terrible idea fyi - speaking to myself!). It almost felt like i was slowly drifting into exhaustion but because it was only one more prompt to write it was hard to see. I think this had such a bigger impact on me than I realized. Disclaimer: obviously i'm not a (Reddit) doctor and this isn't advice, but It felt important to share this post in an effort to help people understand the early signs I was having, how to recover, and what I'm now doing going forward. I took some time to order these into the order they first appeared. Early Signs Mid-Stage Signs Later Signs Bigger Warning Signs Constant urge to check, respond or research stuff Wired but exhausted Tired even after sleeping Anxiety spikes Difficulty relaxing even after stopping work Brain fog Eating less, prioritising work over nutritian Persistent headaches Reduced ability to focus on one thing (because I rarely was) Forgetting small things or losing train of thought Waking up already mentally fatigued My body and mind shutting down Feeling mentally full all the time Needing more stimulation to stay engaged Emotional flatness and less excitement Feeling emotionally numb Slight irritability / emotional sensitivity Struggling to enjoy offline activities Feeling detached from my body and the places I normally feel happy / safe 😞 Inability to stop working even when exhausted More compulsive context switching Feeling restless during quiet moments Small tasks were starting to feel overwhelming Physical symptoms continuing for days Increased doomscrolling during a 'research' session Sensitivity to noise, notifications, or interruptions The recovery: I was out with my friends in at a nice sushi restaurant and I didn't want to eat, I LOVE sushi, headache, fatigue, irritation, sensitivity - i needed to go. So I went home and the girl I'm seeing looked after me whilst I was basically non-verbal. She said it was nice because I'm usually so self-sufficient (thanks Claude). We did the obligatory AI checks, they all agreed, I needed rest (physically and mentally) and re-hydration. What I did was stay in a cool house, NO INTERACTIONS with Claude after the initial research (which was somewhat annoying tbh), went to bed and could hardly sleep at all in the beginning but I was reseting my dopamine system (I think) and only came out for water, dehydration tablets and food. The aftermath: I would have been easy to pass this off as a fever or whatever, but I took a long hard look at what was happening and realised I had to look after myself more (if only to spend more quality time with Claude). But seriously, now I'm starting each day away from the computer and each session with a clear plan (also away from the computer), time boxing sessions to work on single tasks and taking smaller breaks in-between, if there's dead time whilst the agent is working - I'll clean the dishes I was ignoring or grab the clothes drying for 4 days (you get the point), for reddit I'm using a custom tool to avoid too much time on the platform (still love you boo) and overall just paying attention more to myself and my needs. Sorry this has gone on a bit long. But I feel this is important and if you made it this far I hope something sits with you and you don't end up where I was. submitted by /u/BuffaloConscious7919 [link] [comments]

Stop Claude from wasting tokens exploring your codebase [archmcp]

AI coding agents spend a surprising amount of time: crawling files guessing architecture tracing dependencies rebuilding context every session So my friend built archmcp, a local MCP server that generates a compact architectural snapshot of a repository before the agent reads a single file. Instead of starting blind, Claude Code gets structured context about: modules symbols dependencies routes architectural patterns It’s giving AI agents enough architectural awareness to stop wasting tokens and time rediscovering the codebase from scratch. It also supports multi-repo setups, so agents can reason across systems like: Go backend TypeScript frontend Python FastAPI services mobile apps shared libraries Repo: archmcp on GitHub Would love feedback from people who give it a go. submitted by /u/yellow-llama1 [link] [comments]

Why We Build

One silver-lining to the dead internet we're living in, today, is that it's very quickly teaching us that we can't rely on our senses as much as we believe we can. It's not healthy to always live in skepticism, but it is necessary in a World where you don't know what's up or down anymore. That's why we need great minds to focus their attention on solving the problems associated with credible information sharing without it becoming some centralized playground designed to look like the free-flowing exchange of ideas. If we don't solve for that, then I guess we're heading into a future that a small handful of people want because elections or public opinion will no longer matter. One of the biggest focuses in AI should be in figuring out how to get it to provide deep credible knowledge in specific domains that can be best applied to the problems we're trying to solve. Sure, it can do this with enough fenagling, but what I really mean is having something easy for everyone to use like Perplexity or Gemini, only it doesn't simply find consensus information from the internet using all these black box methods that are owned by major corporations. Instead, it should use direct knowledge from domain experts who structure and cite their material and as users, we should be able to backtrack all of it, including the original author. And all of this should be achievable by simply engaging with a chatbot agent that can reliably go out and help me discover all of these things. Also, we shouldn't have to simply trust that the application works. We should be able to go in and see exactly how it's working. This way, the public can audit the systems we're relying on for grounding our worldviews. That, to me, is where we should be if we really want to break from the chains of propaganda and reclaim our genuine thoughts about how we ought to live. The alternative independent media space was co-opted long ago and now all of the feeds keep us in a state of perpetual dislocation from our friends, family, communities, new solutions, and better approximations to the truth. We exist in a walled-off digital pasture. But if regular people who are smart and capable enough decide to leverage this new technology, then we can break through the fencing and finally live in a world where discovery-based researching and learning can be easier than Google, which could eventually individuate society again, like how it was before, instead of keeping us clustered into specific groups based on our viewing preferences. That's why my brother and I got into this business. Yeah, sure, we also wanna make a buck so we can retire with dignity. That's true. But the drive has always stemmed from wanting to figure out a better way for people to share hidden insights and create things that are bigger than they thought they could handle. We have a long way to go, but we're making the first small steps, even if it isn't obvious, just yet. Bottom line, though? Humanity must figure out a way to help us master the means and methods of discovery-based knowledge acquisition, execution, and immediate distribution of information based on relevancy and needs from those who search instead of those who passively soak information in from the curated feeds. And all of this needs to be easy enough for a 12 year-old to do. If anyone else is working on this problem, we'd love to hear your thoughts, even if it's through a DM. We're living in the most exciting times, but with adventure, comes danger. So maybe, idk. Let's make it more fun and less hazardous, so that we can, at least, live long enough to re-tell this great story that we're all a part of. submitted by /u/CyborgWriter [link] [comments]

Small victory using Cloudflare for simple hosting of generated HTML/mini-websites

Something many people are running into: You, or a teammate, have created some kind of mini-website app out of Claude and now want to share it with the rest of the company, without overbaking the hosting solution (e.g. not setting up new Azure app services or containers, etc). Maybe you also need some basic data storage for persistence. And how do you do all of that securely? We recently went down this rabbit hole, while looking at all the major players: Vercel/V0, Lovable, Netlify, Coolify, Dokploy, Github Pages.. and even considered baking together our own hosting app solution using Azure or AWS as the backend. Our target audience is non-technical users in the team, so I was looking for something with drag-n-drop style deployment (no git required), and I really wanted to have SSO for protecting application access, along with some type of DB storage. The main issue I ran into was SSO authentication support being gated behind enterprise-level pricing plans for hosting systems like Netlify (which I'd otherwise highly recommend for a small public project). Netlify's enterprise level quickly gets quite a bit more expensive than their base tiers. I also didn't want to purchase yet another AI platform (e.g. Lovable, where really they're pushing an end-to-end AI development platform where you buy token credits through them). I wanted to host things we're already creating in our own Claude environment. Finally, I ended up on Cloudflare, which I've otherwise not really used before professionally. It's not as non-technical-friendly as Netlify, but it's pretty close. You can deploy Cloudflare Pages content via drag-n-drop. It has button-click databases available for integration, and most critically for us, the SSO integration is completely free for under 50 users. Their free hosting tier is also extremely generous and basically unlimited for completely static apps. Noting that SSO goes up to $7 USD/user/month for over 50 users, so your org size can really make a difference. If you have 500 users and the same use case for "hosting little mini apps", I'd go back to Netlify or another offering where SSO is more of a fixed fee. The other big win was that Cloudflare has a solid MCP server that works perfectly with Claude Cowork. We integrated that in and then wrote up some skills to assist with app building and deployment, including prompts for if a database backend is needed (using Cloudflare D1) and whether the app should be public or internal only with SSO protection. All working perfectly with minimal technical experience required for the enduser. I'm not at all associated with Cloudflare, just thought I'd share how we got a win for this use case. I'd be interested to hear if anyone else solved the same problem in a different way. submitted by /u/flck [link] [comments]

$2,500/mo AI Budget: My friend just burned through 62M Opus 4.7 tokens in 24 hours.

My buddy works for a small international company based in Vietnam, and their AI perks are absolutely insane. Management actively encourages heavy API usage and hands everyone a massive $2,500 USD monthly budget. The screenshot? That’s his dashboard after burning through 62M tokens on Opus 4.7 in a single day. He mentioned some of his colleagues are chewing through even more with 'fast' mode turned on. Honestly, prove me wrong, but I’m pretty sure this small company is offering a bigger AI allowance than most Big Tech giants in the US right now. Anyone at FAANG getting this kind of blank check for API usage? submitted by /u/No-Wheel5791 [link] [comments]

Repurposed my old work ThinkPad as a dedicated personal AI workstation — looking for ideas from people who’ve done something similar

Apologies if formatting comes out weird- I am on mobile. My old employer let me keep a ThinkPad when I left. Rather than let it collect dust, I’m turning it into a dedicated personal AI environment — wiping it, installing Linux, and using it specifically for two things: life admin automation and building personal software tools. The core setup I’m planning: • Claude Desktop with MCP servers running persistently as Docker services • Tailscale so I can access everything securely from my phone when I’m not home • Open WebUI as a mobile-friendly chat interface • Code-server (VS Code in the browser) so I can actually write and run code from my phone • A dedicated Gmail account that acts as the “identity” for this Claude instance — wired into Google Drive, Calendar, and potentially an email-triggered agent pipeline • A local RAG system for personal documents — contracts, notes, research — so Claude has persistent context about my life The idea is that this becomes an ambient personal intelligence layer — always on, always up to date on my documents and projects, accessible from anywhere via Tailscale. Not a cloud subscription, not shared with anything work-related. Fully mine. On the software side, I’m planning to use Claude Code + Lovable to build local-first personal apps for my own pain points — things that don’t exist in the market the way I want them, or where I don’t want my data in someone else’s cloud. The ThinkPad is the runtime; Lovable builds the frontend, Claude Code builds the backend, and everything talks over a local API. What I’m curious about from people who’ve built something like this: • What MCP servers have actually been worth setting up vs. overhyped? • Has anyone built a reliable file-drop-to-RAG pipeline that actually stays current? • Is Open WebUI the right mobile interface or is there something better now? • Anyone using a dedicated “agent identity” email account — what workflows have you actually automated? • Claude Code + local backend: what’s your stack? FastAPI? SQLite? Something else? • Any gotchas with running Claude Desktop persistently on Linux? Genuinely trying to build something useful here rather than a tech demo. Would love to hear from people who’ve gone down this road.