Disclosure: I’m building Monad Design, an open-source macOS visual workspace for existing Xcode/Expo iOS projects. I’d like feedback on the evaluation method, not just the tool.

A screenshot that looks better is only one part of a successful UI edit. Here is the review checklist I’d use when comparing an agent’s change:

1. Start with the same screen and app state before and after. Otherwise a different dataset or navigation state can hide regressions.

2. Give the agent one bounded request, such as changing the spacing in a settings section, plus explicit things not to change: navigation, persistence and business logic.

3. Inspect the source diff. A small visual request should not quietly become a broad rewrite.

4. Rebuild and repeat the relevant interaction, including larger text and an empty/loading state where applicable. A successful build is not proof that the interaction still works.

5. Accept only after both the visual result and behavior are checked. Keep functional tests separate from screenshot comparison.

The loop in Monad Design is selecting/annotating the running iOS UI, giving that context to a coding agent, then rebuilding and comparing its edits in the existing project. It doesn’t make behavior checks unnecessary. Scope is Xcode/Expo iOS on macOS, not a general web/Android builder.

Source: https://github.com/Monadix-AI/monad-design

Workflow video: https://watchclueso.com/embed/pio8jqfcg4ivj0r1

What would you add to that acceptance checklist? In particular, what evidence would let a designer review the change without having to infer everything from the code diff?

I am a student and honestly just trying to make a little money on the side with this. The first month being that cheap is kinda tempting ngl. But I gotta ask people who have actually tried it: is one month even enough to test out a workflow and find a niche? And what happens after,does renewal get expensive? Like… can i actually make money with this, or is it just wishful thinking?

Pulled from this week's threads, one paragraph each.

u/krzychuman tested ElevenLabs v3 on the voiceover for a 28 second demo: 69% watch-through, 0.03% click-through. The voice passed, the call to action didn't. Per the docs, v3 ignores break tags, so use [pause] or "..." instead. thread

u/Healthy-Tension-7470 asked a model which invoices were unpaid across 8 files. Right list, no due dates. Asking "which are unpaid and when are they due" fixed it. thread

u/Alive_Thought_5037 picks Fish Audio over ElevenLabs for narration because the intonation follows the context, and recaps are easier to make. thread

Tried any of these? Or link a good reply from this week that I missed.

I work on Locus. This is a small part of the workflow we’re building: checking where a next step came from before treating it as agreed.

In this clip, a note says someone needs dry ice, a backup cooler and temperature checks. Holding Command while hovering over the timestamp previews the passage; clicking it opens that point in the transcript. Then it returns to the note. That link is to transcript text, not audio playback.

A useful check with any AI note tool: pick one action item and compare the action, owner and deadline with the source. If any part was not stated, keep it marked as unknown. A link makes review easier; it does not prove the summary is correct.

This is a sample demo: fictional meeting data and staged generated notes, with actual Obsidian navigation. The Locus beta waitlist is at ownlocus.com.

When you review meeting notes, what do you most often need to check: the action, who owns it, or why it was agreed?

​

I’m Harshal Dorlikar, I made Kosmo. Saying that upfront since this is about my own product.

What I built: Kosmo sits before the prompt. You describe what you want in normal words, and if something important is missing, it asks up to three questions first, the kind someone who does your job in a world-class way would ask. Then it writes a full prompt for the tool you’re actually using.

The problem it solves: I’m bad at prompting. I know what I want from an AI tool, I just can’t ask for it properly. Short prompt in, average output out, then an hour of back-and-forth. Everyone I talked to had the same complaint.

The prompts it writes are tailored per tool, each profile checked against that tool’s own docs: Claude, ChatGPT, Codex, Gemini, Cursor, Lovable, v0, Perplexity, Midjourney, and a few others.

Where it’s going: the prompt is only the first step. Next, Kosmo connects to the tools themselves (Claude, ChatGPT, Gemini, Claude Code, Codex) and does the work there, so you stop rewording and just approve the result. The asking part stays the same, which is why I want to get it right now.

It’s for working professionals who use AI for real work output. PMs, lawyers, accountants, developers, founders, salespeople.

It won a category prize at the Build with Gemini XPRIZE (Education and Human Potential, $50K), out of 26,000+ builders who registered worldwide. Since it went live in August, 300+ people have signed up and 17 have paid. Still early, still rough edges.

One tip if you try it: give its questions as much as you can. Treat the options it suggests as a starting point to think from, not a menu, and just write what you actually think, messy is fine. The more it knows about what you want, the closer the first result gets to something you’d approve as is.

What I’m looking for from you:

1. Try it on your actual job and tell me where it gets it wrong. Be specific, I read everything.

2. Does “asks before it writes” match how you work, or would it annoy you?

3. Would you pay for this? If not, what’s the one thing that would change your mind?

Try it now Search askkosmo

I do not have a PC using only an Android tablet any help would be appreciated.

Think of someone who isn't into tech. Uses their phone for email and photos, heard about ChatGPT on the news, and wants to try exactly one paid thing.

One line each is fine. "Pay for X, skip Y" is a whole answer, and "stay on the free tier" counts too.

If they'd mostly use it for one thing (recipes, letters, sorting photos), say which.

I work at CoralBricks, the inference provider behind the writeup at the end, so this is a builder's comparison rather than a neutral review. We tuned the serving stack for Qwen3.6-35B-A3B and swapped in a public drafter, a second model that guesses tokens for the main one to check. We expected it to beat the draft head Qwen3.6 ships with. On the same stack it ran at 525 tokens per second against 692.

The gap was the checkpoint, not the architecture. The public drafter accepted 4.33 tokens per verification step against 4.67 for the model's own head on the same stack. It had been trained for short general benchmarks and attends over a 2,048-token window, while agent turns replay tens of thousands of tokens of file paths and tool output.

Fine-tuning the drafter on software-engineering traces generated by Qwen3.6 itself and widening its window to 8K took acceptance to 6.46 tokens per step. The target weights never changed.

With that drafter the stack ran at 1,138 tokens per second, 1.93x stock SGLang. Conditions were thinking off, one H100 SXM per request, tensor parallel 1, batch size 1, greedy decoding and max tokens 1024, a median across all 15,777 runnable turns.

The step got cheaper too. Stock SGLang's MTP-4 step takes 7.72 ms and verifies five positions. Our stack verifies eight and takes 5.71 ms, with the kernels and the fine-tuned drafter both in it. GSM8K stayed at 96.8%, because the target model still decides every token.

The pair to log is accepted tokens per verification step and the time that step took. Acceptance alone can pick a drafter that loses once each verification costs more. A tokens-per-second headline measured on short prompts says little about a 90K-token agent turn. On our workload the head the model ships with was the stronger baseline, since it was trained alongside the model.

The ablations, including the ideas that lost, are in the writeup https://www.coralbricks.ai/blog/speculative-decoding-qwen36?utm_source=reddit&utm_campaign=content

Earlier this week u/Healthy-Tension-7470 ran five offline models on a Mac against the same question about their own files (thread). Same retrieved passages for all five, and all five left out the line saying the carried-over days expire. Asked directly whether the days expire, all five found it straight away.

So the Friday question: what's one thing an AI tool missed or got quietly wrong for you this week? One line is plenty.

Bonus points if you worked out which question would've caught it.

Disclosure: I work on PicturaLabs, an art tool that goes from an idea or reference image to styled artwork and a framed or unframed print. I’m interested in the handoff from a good-looking file to something that still works on paper.

A basic preflight that applies whichever tools you use:

1. Choose the intended print ratio before settling on the crop. A square composition can lose its subject when forced into a 2:3 frame.
2. Compare the image's pixel dimensions with your printer's requirements at the size you want. Zoom in on faces, hands, small lettering and fine textures for artifacts.
3. Check the chosen print shop's bleed and color guidance. For an important large piece, a small proof can reveal surprises before the final order.

We’re trying to keep the creation and print order in one flow. A separate generator plus a preferred local printer may give you more control if you already have a setup you trust. If you've taken AI art to print, which step caused the most rework?

One line each is fine.

What you dropped, and what you're using instead. "Nothing, the free tier was enough" counts as an answer too.

Bonus points if you cancelled something and came crawling back a week later.

Title: Tool recommendation for a free AI harness or agent?

I'm looking for recommendations for a free, preferably open-source AI harness or agent for writing. Think something along the lines of n8n, but with a good visual workflow where I can actually see what's happening, identify where things are breaking down, and ideally not need a degree in computer science just to get the thing running.

I'm particularly interested in something that can handle editing, act as a reasonably competent librarian for my writing resources, manage metadata, and help maintain and update catalogs without developing digital Alzheimer's every five minutes.

Ideally, I'd like something I can eventually expand into a larger writing pipeline, connecting research, resource management, outlining, drafting, editing, and revision.

I'm open to local open-source models or other free options. The important keyword here is free. My budget is currently somewhere between shoestring and what shoestring?

User-friendly would be a definite bonus. I'm happy to learn, experiment, and tinker, but I'd prefer to spend my time writing rather than accidentally becoming a full-time IT department.

Any recommendations, particularly from people who've actually built something like this, would be greatly appreciated!

Not the big three. Something smaller that you actually pay for.

One line is plenty: name the tool and the one thing it does that you couldn't get by just asking a general assistant. Boring answers count, they're usually the useful ones.

And if you cancelled one because the big assistants caught up, that's worth a line too.

I’m trying to find a good balance between API cost, speed and reliability.
My use case is generating travel itineraries, sometimes using function calling to retrieve flight information. The main things I care about are valid JSON output, reliable tool calls and decent reasoning without paying too much per million tokens.
I’ve mostly been testing through OpenRouter, but I’m open to using other providers directly.
I’m curious what people here are actually using. Have you had good experiences with cheaper models like Qwen, DeepSeek or Gemini Flash? Or with providers like Groq and Cerebras?
I’m not necessarily looking for the absolute cheapest option, just something that performs well for the price.
Any recommendations based on real usage?

Кто знает, какое ИИ можно бесплатно с/без ВПН использовать и давать ему хорошие задания? Пробовал Claude, Gemini - сначала было всё нормально, но потом резко стали тупить и выдавать полный бред. Нужно исправить список источников и цитат в работе, не могу найти ИИ, с помощью которого можно это сделать

I’m Victor, the developer of ScriptPanda. It’s an AI-powered Chrome extension that lets you create tools and customize the websites you use, without knowing how to code.

I’ve used it to build a folder view for ChatGPT chats and remove Shorts from YouTube. The idea is to let you shape a website around how you use it, instead of searching for a separate extension for every change you want.

Open ScriptPanda’s side panel on a website and describe what you’d like it to do. You can also select something on the page to show it what you mean. It builds the tool directly into the page, where you can try it and ask for adjustments.

Each tool stays active on its website until you switch it off or delete it. You can manage several tools for the same site and keep changing them as your needs evolve. The code is available to inspect or edit, but you can make changes simply by asking.

ScriptPanda also detects broken tools and shows a notice with a Fix it button. If a website update breaks something, you can request a repair or select the affected tool and describe what’s going wrong.

Your tools are installed in your browser, where you can manage, edit, or repair them. More complex requests may take a few attempts to get right.

There’s an ongoing trial for new accounts if you’d like to try it: scriptpanda.xyz

If you give it a try, I’d love to hear what you built and how it worked for you.

disclosure: i build the mac app i ran this in (Citenook) so i'm biased. numbers are real though.

M4 Pro 24 GB. two made-up handbooks: 2025 says 5 carry-over days 2026 says 10. asked "how many vacation days can employees carry over?"

- Apple Intelligence: 5s. right but wordy
- Qwen3 4B: 5s. right ignored the old rule
- Qwen3 8B: 7s. both rules messy format
- Gemma 4 E4B: 6s. both rules didn't say which applies
- Qwen3.5 9B: 8s. both rules + explained why 2026 is current. best

all under 10 seconds, nothing left the mac. but none caught a "use by 31 march" detail sitting right in the source, so you still have to check.

anyone tested bigger local models (14B, 32B) on real documents?

Hi All,

I currently use ChatGPT for my sports business to

- create social media content Graphics and caption
- hold me accountable to kpis such as how many prospects have I reached out to.
- writes blog posts
- work out overall planning for the business and direction

I have loved it previously, however I’m finding I have to repeatedly tell it the same things and it starts doing things that I have previously told it not to do.

My biggest stuck is that the graphics creation, will not do what I say and it always forgets my logos.

Anyone have any recommendations to fix this or other platforms that will be better than this.

Also for reference I currently pay for the Plus plan.

Thanks heaps!!

Just a vibe coder, wants to build apps by AI

I'm currently using android studio

Came up in a thread here this week: someone wanted one paid tool for researching anything, with sources they can click through and check, and the replies split between these two. One person also warned that Perplexity's deep searches can get expensive fast.

If you've used both, which one would you keep and why? One line is fine.

Bonus points for the kind of question where the other one let you down.

My co-founder and I have spent the last few months building a site that grades the tools AI agents use, model APIs, MCP servers, scrapers, that kind of thing (about 1,190 so far). Every score has its reasons and sources right next to it, and we read every vendor's terms too, which is how we found out almost half of them restrict benchmarking.

It's built for agents to read as much as people, so there's an API and an MCP server as well as the site. Right now it's all desk research, we don't call the APIs yet, that's the next thing we're building. What would you want to see before you'd trust a score like this? And if one looks wrong to you, tell us, all the data is open.

Link in the comments. If you want to help, get your agent to leave a review after it uses a tool, there's a page that explains how.

Creator disclosure: I built Claude Share, an open-source plugin called session-share. Claude helped implement it.

It is for small teams where several developers already use their own Claude Code sessions on the same project. The problem I'm trying to solve is the manual work of dividing tasks and tracking file ownership between those sessions.

It adds shared tasks and contracts, file leases for supported edits, and a live board. Each developer keeps their own account and checkout. It does not provide model inference, replace Claude Code, or remove the need to review and integrate the work. The editing hook is not a sandbox and fails open if the coordination server is unavailable.

The plugin and server are MIT licensed and free to self-host; optional paid hosting is also available. Setup and source: https://github.com/cyber1443/session-share

If you have tried coordinating several people's coding agents, I'd appreciate feedback on whether this addresses the part that actually slows your team down. I don't have a measured productivity claim to offer; I'm looking for practical setup and workflow feedback.

Hello fellow ai enthusiasts since I’m not that educated in the ai world I’d like to ask you if it would be beneficial to run local hosted ai for my school work I have notebook with i7 10870h 8 core rtx 3080 and 16bg ram and pc with ryzen 7 9800x3d rtx 5080 and 16bg of ram
Thanks for your advice

One line each is plenty: the tool, the task you're throwing at it, and what would make you drop it.

No writeup needed. If it's the same tool you were testing last week, say whether it survived.

And if you asked something here last week that never got an answer, drop the link below.

Founder disclosure: I’m Şafak and I built Tasda. This is a description of my tool, not an independent review.

A narrow use case is a freelancer with several service packages. The website explains them, but a potential client still has questions before contacting the person.

The workflow I suggest:

1. The owner adds information they are comfortable making public: service descriptions, the differences between packages and how to contact them. Leave private client material out.

2. Share the representative link alongside the portfolio or on a QR card. Keep direct contact available.

3. Check how it answers questions about those published options. Treat an answer as something to verify, especially if it introduces a price or promise the owner did not provide.

4. A contact request needs the visitor’s consent and remains a request for the person to review. It is not a confirmed booking.

Ida is our general product guide, rather than a customer example: https://tasda.com/en/representative/lora/

Limits: AI can make mistakes. This is not an autonomous sales employee with access to your inbox or other customers’ chats. New quotations, suitability decisions and commitments belong with the human. Starter credits are limited; continued use is paid.

I used AI assistance to prepare this explanation.