PH热榜 | 2026-03-22

Hey Product Hunt! 👋

I’m Manuel, co-founder of Silverstream AI. Since 2018, I’ve been working on AI agents across Google, Meta, and Mila. Now I’m building Bench for Claude Code with a small team.

If you use Claude Code a lot and want to store, review, or share its sessions, this tool is for you. Once connected, Bench automatically records and organizes your sessions, letting you inspect and debug them on your own or share them with your team to improve your workflows.

Getting started is simple:
• Go to bench.silverstream.ai and set it up in under a minute on Mac or Linux
• Keep using Claude Code as usual
• Open Bench when you need to understand or share a session

That’s it.

Bench is completely free. We built it for ourselves and now want as many developers as possible to try it and shape it with us.

We’ll be here all day reading and replying to feedback (without using Claude 😂). Would love to hear what you think!

Btw, support for more agents is coming soon, so stay tuned!

I’m curious how detailed the tracking is. If I can really see every tool call and file change clearly, I can imagine using this for debugging more than anything else.

Claude Code is so capable that we end up trusting it a little too much. But that's exactly when things get interesting:

• I've had it silently migrate my local DB to an incompatible version while fixing a bug.

• Another time, Claude decided the only way it had to fix an issue with a particulary inefficient for loop, was to turn off my audio drivers.

The real problem isn't that it made mistakes. It's that I had no way to go back and understand what it did, when, and why, to learn from it and finetune my prompts. Sure, I could just scroll the claude logs, but what if the "failures" weren't apparent until much later? Or what if the issue was at step 315 out of an hour-long agent run of 500 steps?

That's why Bench is a big deal. Not just a logger, but an audit trail that makes agent actions legible: every tool call, file change, conversation, subagent detail, all is there for you for as long as you need it, searchable and shareable. A great way to "share your context" to your colleagues, as well as being what I really needed to learn from my mistakes and improve in terms of prompt writing!

How deep does it go when tracking tool calls and file changes across a session?

I’ve been using Claude Code quite a bit, and I often lose track of what actually happened in a session. This idea of being able to go back and inspect everything feels really useful for me.

Being able to attach session history to PRs is a really smart idea. Makes collaboration much easier.

Hey folks! I’m Simone, Co-founder and CTO of Silverstream AI.

Really happy to be launching this today. I’m excited to share it, and very curious to hear your feedback!

One habit we’ve introduced across the team is linking Bench sessions in PRs whenever Claude Code was involved in creating or debugging a change. It gives reviewers a lot more context on how a bug was found and fixed, instead of just showing the final diff.

That’s been one of the most useful workflows for us, and I’d recommend it to other teams using Claude Code too.

I’m also using Bench in a research setting, where session data helps generate detailed methodology reports showing how results were obtained. I’m already finding it useful, and I think there’s a lot more to unlock there!

Looking forward to your thoughts. I want to make Bench as useful for other devs as it's been so far for us, and your input really matters!

Now add observability + failure handling, otherwise it’s just scheduled guessing.

How granular is the session tracking? Can you trace decisions step-by-step or it is more of a high level overview?

Nice.

Most people don’t need logs.

They need to understand why the agent made a bad decision and how to prevent it next time.

Cograts on the launch. I can see this becoming essential for teams using AI agents regularly, especially when debugging or reviewing work.

I've tackled similar challenges with code reviews and context sharing, and I love how Bench automates session storage. How do you handle sensitive data in stored sessions to ensure developers aren’t accidentally sharing proprietary code?

I love finding Claude Code related products daily on PH. This looks great!

How granular is the session tracking? Can you trace decisions step-by-step or it is more of a high level overview?

Hey Product Hunt! I'm Omar, Founding Researcher at Silverstream AI.

We originally built Bench as an internal tool to make debugging our own agents less painful, and it's become something I reach for every day.

My favorite part? The high-level run overview. When an agent run has hundreds of steps, being able to scan the whole thing at a glance and immediately spot where something went wrong is a huge time-saver. From there, I can zoom in all the way down to the model's reasoning traces at the exact step where things broke, which makes a real difference when you're trying to understand why an agent made a certain decision, not just what it did.

As we kept adding features, we realized Bench had become too useful to keep to ourselves, so here we are! 🚀

We're starting with Claude Code, but support for more agents is on the way. Give it a try and let us know what you think!

Hi everyone! 👋 I’m Giulio, co-founder and COO at Silverstream AI.

It feels like we’re all trying to buy back time these days. There’s always more to do, and never enough hours. That’s why I really think tools like Bench for Claude Code matter.

Agents are getting better fast, which means longer and more complex sessions. Hopefully more reliable too. But even as trust increases, I don’t think we’ll ever fully give up control. We’ll always want the option to see what they’re doing, as long as it doesn’t slow us down.

That’s exactly what we’re building Bench for.
If you try it out, I’d really appreciate your feedback. It’ll help us shape our product in the right direction.

I've been thinking about this for a while now. Traditional git style version control is not optimal for the AI coding era. You lose information from your claude code terminal or your AI coding tool of choice. Cool to see this getting productize. Congrats on launch!

Great looking observability layer to see what's happening behind the scenes! I think it will surely help teams optimize their processes.

Congrats on the launch!

Congratulations on the launch 🎉 🎉

Storing and reviewing sessions sounds like a developer convenience. But what's actually happening is something more interesting — you're creating a layer of reflection between execution and understanding.

Most tools help you move faster. This one helps you see what you did. That distinction matters more than most people realize, because the gap between building and knowing what you built is where most coordination breaks down.

This is useful. I use Claude through Cursor daily and half the time I wish I could go back and review what it actually changed across a session. Being able to store and review sessions would save a lot of second-guessing.

So basically you can use this to correct your other AI or just Claude ...

I’m excited to hunt this because Claude Code is moving from a tool you open to an agent that actually works for you on a schedule.

What it is: Scheduled Tasks in Claude Code now across both local desktop and remote execution.

Problem → Solution: Repetitive coding and ops tasks require manual effort or complex setups (cron, scripts). Now you just write a prompt, set a schedule, and Claude handles it automatically.

What’s different: It works both on your local machine (while it’s awake) and remotely on cloud so tasks can run continuously without you needing to keep things open.

Key features:

• Set repos, prompts, and schedules

• Recurring task automation

• Runs locally or cloud-based

• Access to your code, workflows, and tools

Use cases:

• Automated reports & updates

• Log monitoring + PR creation

• Research and data collection

• File cleanup & workflow automation

Who it’s for: Devs, founders, and teams who want agent-like automation without infra overhead.

To get started:

https://claude.ai/code/scheduled

https://code.claude.com/docs/en/scheduled-tasks

https://code.claude.com/docs/en/desktop#schedule-recurring-tasks

P.S. I hunt the latest and greatest launches in tech, SaaS and AI, follow to be notified → @rohanrecommends

I asked Claude himself to give me a summary of the feature :

Recurring Tasks (Cron) in Claude Code:

• Schedule prompts on a recurring interval or as one-shot reminders

• Works in both CLI and VS Code

• Limits: session-only (nothing persisted to disk), auto-expires after 7 days, only fires when Claude is idle

• Use cases: CI polling, one-off reminders, temporary monitoring

• Verdict: session convenience tool, not a real scheduler

Setting up cron jobs for AI tasks was genuinely painful before this. Write a prompt once, set the schedule, and let it run — locally or in the cloud. This is what "agentic" actually looks like in practice 🔥

@rohanrecommends @brooke_dewitt1 @scott_white_sf
Im keen to understand this. Is this essentially an AI agent that will independently go and complete tasks?
Im currently on Max plan and have Co-Work enabled. How is scheduled tasks different?

been wanting this for a while - I've got a few repos where I manually kick off the same Claude Code prompts like 3x a week. setting schedules once and forgetting it is exactly how it should work. curious how the cloud piece handles auth - does it need separate Claude API keys per environment or does it pick up from local config somehow?

Bridging the gap between local cron jobs and cloud environments usually requires maintaining entirely different setup scripts. I would definitely use this to manage my automated database backups so I can test the intervals locally before pushing them directly to production. The unified syntax alone will save developers a lot of unnecessary debugging time.

Scheduled recurring tasks is something I didn't know I needed. I could see this being great for automated code reviews or daily build checks. Does it support notifications when a task fails?

Nice one, excited to try it!

Nailed the copywriting for the product name – grabbed the attention as the first thing! :D :)

It might help to show real examples of sites at each level. That would make the scoring system more tangible.

Tried this via Claude Code ( Evaluate our landing page via siliconfriendly.com/llms.txt ) and it worked surprisingly well. Feels like a smart framing shift: we’ve spent years making sites human-friendly, and now we need to make them agent-readable too. The report runs inline and I never had to leave the CLI session, very nice. Congrats on the launch.

I ran one of my projects through your analytics and got some interesting insights. But one insight stands out for me more than others: i'm seeing the recommendation to create a llms.txt file. Is there any real evidence that this file actually does anything? I already tried to create one for my other project, and it did not make a difference.

Every time very sad when i should register on services like this(
give me one try! i just wanna check!) i don't want register.

Quite nice! Btw on what basis have you considered these levels? Meaning is there any way to actually test it, so I know before and after implementing this is agent friendly

This sounds innovative and relevant! Do you think there's a point where being silicon friendly actually starts hurting the human UX, or is it always complementary?

Ran this on my own site and got L1 passed, L2 failed. Didn't even have a robots.txt set up, so that was a useful wake-up call. The level breakdown makes it really clear what to fix first instead of guessing. Curious if there are plans to show suggestions next to each failed check?

The idea that agents are becoming just as important an audience as humans is something we don't think about enough. Really interesting way to frame it!

Taking it Shubham! Really got the concept and I'll be trying it soon for many of my projects. Congrats on the launch btw

ran our main product landing through it and got L2 - robots.txt was there but no llms.txt and structured data was thin. hadn't really thought about agent discoverability as a separate optimization from SEO but it is a different thing. the L0-L5 framing makes it actionable vs just a vague checklist

Just ran my own site through this and honestly the report surprised me. I thought I was doing well with llms.txt and Schema.org structured data but turns out I'm L2 with no public API or agent.json, which I hadn't even considered. Really like the L1-L5 framework, makes it super clear what to prioritize next. The competitor comparison in the PDF was a nice touch too. Congrats on the launch!

I was thinking about this yesterday, how to make my site more agent friendly. Great work.

Think of it like a PageSpeed score but for AI agents — brilliant framing. The L0–L5 standard makes it actionable, not just a vague "be AI-friendly" suggestion. Already curious where most sites land 😅

Congrats on the launch! The L0-L5 framework is a smart move, giving it a standard makes it something the industry can rally around, rather than just a one-off tool.

Curious where most websites are landing right now. Is the average closer to L0, or is there more readiness than expected?

This is such a timely concept. As someone building browser-facing tools, I've seen firsthand how wildly different websites behave when agents try to interact with them — some are basically navigable out of the box, others fight you at every step with dynamic rendering, anti-bot walls, and inconsistent DOM structures.

The L0-L5 framework is a smart way to standardize something that's been totally ad hoc until now. I'm curious: what are the biggest factors that separate an L3 site from an L5? Is it mostly about structured data and clean markup, or does it go deeper into things like providing machine-readable APIs alongside the UI?

This is a quietly brilliant framing. "Silicon Friendly" sounds like a developer tool. But what it's actually measuring is something deeper — how well your website communicates with systems that think differently than humans.

We've spent decades optimizing for human attention. Now we need to optimize for machine comprehension. And those are fundamentally different design problems.

The shift isn't just technical. It's philosophical. The websites that win the next era won't just be readable by AI — they'll be understood by it. And the gap between "readable" and "understood" is where most of the internet still lives.

This tool is measuring a layer of readiness most people haven't thought about yet.

I’m curious how do you envision this report helping non-technical folks implement the recommendations?

Hey PH! Yahia here, founder of Context.dev (formerly Brand.dev).

We've been building this API for a while now and the rebrand reflects where the product has grown; from brand data into a full web context layer. One API to scrape, enrich, and understand any website.

The problem we kept seeing: developers waste weeks stitching together scrapers, enrichment tools, and data providers. We wanted one clean API that just works.

Would love your feedback. Happy to answer any questions!

The best product for getting anything from the internet for your product! Congrats, Yahia!

Congrats on the launch :) Been building something that scrapes 16 different data sources per domain and the hardest part is always the cascade when one provider fails and you need to fall back without killing latency.

This looks like it could simplify a lot of that. How do you handle sites behind Cloudflare or heavy JS rendering? That's where most of my pain is.

This can be a real time saver, i'm a developer and i often end up to writing a different scraper each time. Having a standardized API to extract content from websites is a really interesting solution.

Developers waste so much time stitching together scrapers, enrichment tools and retries — having all of that collapsed into one clean API is a genuine time-saver. Love the rebrand too, "Context" nails what it actually does now 🙌

"Scrape, enrich, and understand" — the first two are infrastructure. The third is where it gets interesting.

Understanding isn't just data processing. It's the layer where raw information becomes something a system can act on with judgment. Most APIs stop at delivery. The ones that matter are the ones that reduce the cognitive load between receiving and deciding.

Curious how you think about the difference between enrichment and comprehension — because that gap is where most products quietly fail.

❤️ Today, we're launching the @Edgee Claude Code Compressor.
I want to show you what it does with a real-world test scenario, so I recorded this video.

I created two separate Claude Code sessions, each connected to a dedicated plan. Same codebase, same task, same instructions: one side standard Claude Code; the other routed through Edgee with compression enabled.

Left side stops at 21 instructions. Right side reaches 26.5.

+26.5% more session before hitting your plan limit.

Here's how it works: Edgee sits between Claude Code and the Anthropic API. Before each request is sent, it strips redundant context, deduplicates instructions, and sends a leaner prompt. Claude sees less noise. You get more range.

To install: curl -fsSL https://install.edgee.ai | bash

Then: edgee launch claude

That's it. Free. Takes 30 seconds to set up.

If you're a Claude Code user who's hit the plan wall mid-task, this is for you. If you're running Claude on Anthropic's API and watching your token bill grow, this is also for you.

We've been in beta for a few weeks. Today it's out for everyone.

More tokens, fewer plan interruptions 🙌

Would be great to see a breakdown or visualization of what’s being removed vs kept. That could help build trust in the compression layer.

Using Edgee already, really great product.

Super simple idea but actually makes a difference on costs

Super useful in this day and age :) Thanks Sacha and team !

Hello Product Hunt 👋 I'm Fernan, the founder of Embedful.

We created Embedful to solve a common problem product teams face when trying to show analytics to their users. Most existing tools either have a steep learning curve, are too specialized for analysts or developers, are expensive, or require significant engineering work just to embed a chart, table, or dashboard.

Embedful makes customer-facing analytics simple and easy. You can create beautiful, interactive charts, tables, counters, and dashboards in minutes using Google Analytics, Google Sheets, Excel, CSV files and APIs. Everything is fully embeddable and shareable. Even non-technical users can visualize data with a few clicks and customize dashboard layouts with drag-and-drop simplicity.

We built Embedful to be fast, flexible, and product-ready from day one. I'm excited to share it with the Product Hunt community and would love your honest feedback.

I will be here to answer any questions! Thank you in advance!

Most analytics tools are built for internal teams. This feels like it’s designed for the end-user experience first, which is a big shift.

How flexible is the embedding layer? Can teams control permissions, user-level data visibility, or dynamic filtering per user?

Congrats on the launch! The embedded analytics angle is smart; most tools make you send users somewhere else to see their data when it should just live inside the product.

With CoreSight, we generate a lot of structured financial output, and the question of how to make it visually digestible without a heavy engineering lift is something we think about. Does Embedful handle data that updates in real time, or is it more suited to static snapshots?

The branding customization is a nice touch too; nothing feels worse than sharing insights that lack a cohesive look. Curious, how do you handle performance when embedding large datasets?

Congrats! We've been looking for a way to share clean dashboards with investors and partners without giving them access to our internal tools. Does the data refresh in real-time when the source sheet updates, or is there a delay? And can you password-protect individual embeds, not just full dashboards?

Does it also allow for creation of a custom dashboard from the shard files or must we edit the sheet columns first before sharing the files?

Congrats on the launch! I've been living in Notion and Google Sheets and knowing I can pull both into one embeddable dashboard is huge! Can you connect multiple data sources to a single visualization?

Nice tool guys. Does it support Firebase or Firestore as a data source, or only the listed integrations?

Really like this idea. Being able to update data using just a connected Google sheet is a great time saver. Any way to integrate with zapier?

Congratulations on the launch! Very nice idea; will try this out.

The "out loud" part is what separates this from every other interview prep tool. Reading your answers in your head feels fine. Saying them out loud is where you realize they fall apart.

The dynamic follow-up questions are the real value — scripted Q&A practice gives you false confidence because real interviewers never follow the script. Someone who can handle unexpected follow-ups is genuinely better prepared.

Curious whether it adjusts difficulty based on how well you're answering — like if someone nails the first few questions, does it push harder or stay consistent throughout?

Congrats on the launch, this is one of those tools that could genuinely change outcomes for people.

Congrats on the product and the launch! This is super useful, especially considering the number of layoffs from the last couple of months.

Does it only tackle questions related to the job description, or does it also help you prep for the other parts of the interview (questions that are more related to your work vibes, style, preferences, or other complementary questions that come up besides the ones related to the job specs)?

Congrats on the launch! The follow-up questions are what make this genuinely useful; most interview prep tools let you get away with vague answers because they never push back.

Curious how it handles role-specific depth, like technical or case interview formats, where the follow-ups need domain knowledge to be realistic?

Congrats for the launch. I think most AI interview tools optimize for sounding good rather than actually being good.

One question: where does the audio and transcript data live? When someone is practicing answers about their weaknesses or salary expectations, that's pretty sensitive stuff. Is it processed locally or sent to a cloud API?

the follow-up questions are the hard part to get right - most interview prep tools just give you a static answer rating but real interviews are adversarial. the dynamic follow-up is what makes you actually think on your feet. does it adjust difficulty based on how you answer or is it the same question tree regardless?

The point about tools that help people "get through" interviews without actually getting better is a meaningful distinction - that's the core problem with a lot of AI interview prep out there. Practicing out loud is the most neglected part of real prep since most people rehearse in their heads and never hear where their answers actually fall apart. @shraddha_sunil have you noticed particular types of follow-up questions that candidates tend to struggle with most when they're practicing out loud?

Sounds nice!
Love that you focus on getting better rather than memorizing scripts. Have you thought about expanding beyond job interviews? Like pitch practice for founders or networking conversations? Feels like the same muscle.

As someone who interviews people fairly often, the gap between someone who memorized answers and someone who actually thinks well under pressure is immediately obvious. Curious what patterns you noticed in the feedback that surprised you most during testing?

Looks solid, but I'd love to see more integrations with commonly used tools like Slack and Figma.

This looks awesome @jnchirag! What all sources can I get feedback into this platform from?

@jnchirag Congrats on the launch, Chirag — this is a very real problem, especially the WhatsApp + email chaos.

One thing I’ve seen with agencies is that even when better tools exist, the biggest friction isn’t features — it’s client behavior.

Clients still default to WhatsApp, scattered feedback, and informal approvals.

Curious — how are you thinking about handling that transition layer?

Because that’s usually where adoption either works really well… or completely breaks.

The struggle is so real! How does the client-facing side work exactly - do clients get their own login to leave feedback, or is it more like a shared link they can comment on?

Congrats on this launch! The scope creep flagging is what stands out so much for me. This is usually the thing that kills timelines and client relationships before anyone even notices its happening. Having one workspace per client instead of scattered WhatsApp threads and emails chains is how you actually run a clean operation. How does the client approval flow work? Can clients review and sign off directly inside the platform?

Love this, finally solving a problem everyone just tolerates. Clean and needed 👏

How do you handle users who only show up when they're nudged? Clients who aren't in the tool daily seem like the hardest retention problem, curious whether you rely mostly on email notifications to bring them back or if there's something built into the product itself that creates a reason to return.

Congrats on the launch! The logo and branding are very slick and well-done. The AI Chatbot is also really helpful in smoothening the struggles of a business owner that cant manage all at once. I wonder whether you are planning any bank integrations? E.g., a smb owner should be able to connect their account directly rather than having to log their own expenses or income. Maybe this can make the product more native and fluent for many.

The insight about local business owners logging data manually without knowing what to do with it is spot on - there's a massive gap between data collection and actual financial decision-making for small businesses. Making finance conversational through natural language queries removes a real barrier for founders who aren't accountants but still need to understand their runway and cash flow. @melikekaya what's the most common financial question you're seeing small business owners ask Montty first when they start using it?

It’s been an incredible journey working alongside this visionary team to turn financial chaos into smart solutions! ⚡️

While focusing on Montty’s strategic growth and product vision, we’ve witnessed firsthand how to leave the clunkiness of traditional banking behind and transform data into real fuel. Finance is no longer just about security; it’s now holistic, personal, and human-centered.

What do you think is the biggest gap in modern finance? Looking forward to starting the conversation in the comments! 👇

The flow is very well taught. Adding customers has been made easy with multiple methods. But i think the crucial mistake for you could be positioning the app against really popular apps like xero and quickbooks as they already started ai support. also they are really cheap. Maybe try to find a way to integrate to tools people already use. Very good work overall, congrats.

If a user's expense pattern suddenly spikes in one category
say payroll doubles in a month
does Montty proactively flag that as an anomaly, or does it only surface insights when the user asks? The difference between reactive and proactive intelligence is huge for your core use case.

Congrats on the launch! The observation about local businesses logging data by hand but not actually using it for decisions is exactly the right problem to start from.

Curious how Montty handles businesses with very inconsistent data, gaps in records, irregular revenue. That's usually where CFO-level insights break down fastest. With CoreSight, we deal with something similar when SEC data is incomplete for smaller companies.

Congratulations,@melike_kaya. The tagline (Finance for Founders, No Accounting Degree) is a great hook for founders.

I analyzed the homepage and saw something worth mentioning.

You have a freemium plan and a Pro plan with a special offer for PH. That's awesome.

But this is what grabbed my attention. The pricing section is clear... but the value of what founders actually get is buried. "Chat with your AI CFO" and "financial health score" are your strongest features. They tell a founder what they really get: peace of mind. Right now those features are listed under "what's coming."

And a founder scrolling might think it's not ready yet.

So I suggest you to move those up. Let them see the real value before they hit pricing.

Took a screenshot to show what I mean and attached below.

When a founder asks 'can I afford a new hire' is the AI pulling from a pre-built financial model template in the background, or is it dynamically constructing the logic from the user's own uploaded data each time? Because those two architectures have very different accuracy ceilings.

When Montty gives a forecast, does it surface any confidence interval or uncertainty range like 'your runway is 14 months, but could be 11-17 depending on churn variance'? Or is it always a single point estimate? Because a false precision problem could seriously mislead a founder.

Does the AI CFO retain context across sessions like if I told it last week that I'm planning a fundraise in Q3, does it factor that into this week's runway answer? Or does each conversation start cold?

When a founder asks whether they can afford a new hire, is the AI pulling from a pre-built financial model template in the background, or is it dynamically constructing the logic from the user's own uploaded data each time? Because those two architectures have very different accuracy ceilings

Congrats on launching, and great name. What's the vision behind it?

This is a clever solution for keeping AI agents chugging along while you’re away; love the focus on productivity with open-source transparency!

I work on a Mac and always use Caffeine for this, but it would be awesome if it was native when I need to work. When the agents finish their tasks, Caffeine doesn't stop, so this is actually a good idea if it can be done. Thanks for the product, I think it looks really cool.

The image of people physically carrying their laptops around the office just to keep AI agents alive is a great product insight hiding inside a ridiculous behavior. A free, open-source menu bar fix that handles this without compromising screen lock is exactly the right scope. @jollife are you planning to add detection for more tools beyond the current list, or is keeping it minimal and single-purpose part of the design philosophy?

Ha, the image of people wandering the office with open laptops just so their agents don't die is painfully relatable. I've definitely done the "prop the lid open and hope nobody closes it" move while running long Claude Code sessions.

Love that it's open source and detects specific apps rather than just blanket-preventing sleep. The fact that it re-enables sleep automatically once the agent finishes is the key detail — most caffeine-style tools just keep your Mac awake forever and you forget to turn them off. Smart to build this as a menu bar app too, keeps it out of the way. What made you choose to detect running apps vs. monitoring actual process activity?

this solves a genuinely annoying problem - had claude code lose progress mid-run because the screen locked and the terminal session dropped. lock screen shouldn't interrupt a running agent but it does. does it detect agent activity automatically or is it always-on while the app is open?

Congrats on building and launching!

After working with a couple of startups already, I think such products are super helpful, as early-stage teams usually don't have the resources (neither the time nor the budget) to invest in the process professional agencies have for developing the brand identity.

I'm also someone who pushes startups to focus on building the business and the brand together, not put a pause or postpone the moment when they start investing in brand building. I'm super curious what industry is currently the most common on Brand Maker?

Congrats on the launch!

The concept is genuinely compelling. Taking someone from brand identity through logos, mascots, social assets, and mockups in a cohesive flow is a real problem worth solving, and it shows you've thought about what founders and creators actually need.

I do want to share some honest feedback as someone who went through the full experience:

The copy on the landing page sets a strong expectation

"Your complete brand system in under three minutes" with a form right underneath it naturally leads you to believe something is about to happen. So going through the full form (brand name, description, visual style, industry) and then landing on a pricing screen felt like a bit of a surprise. I don't think free is owed here at all, but a heads up somewhere before the form, or even a "here's what you get on each plan" framing upfront would make that moment feel a lot less abrupt. The investment of filling everything out first makes the paywall land harder than it probably needs to.

The manual brand creation is honestly a standout. Defining personality, tone, color system, logo style, and mascot style gives users real creative ownership, and I appreciated that. Custom color support is a nice touch as well!

One thing to flag: I hit a "logo failed" error after completing the manual flow, which left me unsure of where I stood. Combined with some copy that felt like it might not be fully localized, the onboarding flow overall could use a bit more clarity and polish to match the ambition of the product.

The foundation here is strong. Hope this helps! 🫶

This is interesting timing for me - I just went through the whole process of building a brand system for my product from scratch. It took way longer than the actual product development at some points. The hardest part wasn't making individual assets look good, it was keeping everything visually consistent as the brand evolved. Do you think your product could help me? For example, if I change one color, will the entire brand palette adjust accordingly?

Big congrats on the launch! Building brand consistency from scratch as a startup marketer is honestly a full-time job on its own. Can you update Brand DNA later as the company evolves without having to regenerate all your assets?

Hey Product Hunt! 👋

We are Aravind & Sai, builder of Nirixa (निरीक्षा — Sanskrit for
"to observe").

I built this after watching a founder friend get a
$4,200 OpenAI bill with zero idea which feature caused it.
They had no way to know. That's the problem Nirixa solves.

What we've built:
→ Token cost breakdown by feature, user & endpoint
→ Prompt drift detection (alerts when quality shifts)
→ Hallucination risk scoring per request
→ Works across OpenAI, Anthropic, Gemini, Groq & more
→ 1 SDK. Under 5 minutes to full visibility.

Launching today with a free tier (100K
tokens/month).

Two things I'd love from this community:
1. Try it → nirixa.in (free tier)
2. Tell me what you'd want to see next

Happy to answer any questions below! 🙏

cost tracking per feature is the thing I've been missing - I know our total Anthropic bill but have no idea which of our 5 products is eating most of the budget. the hallucination risk scoring is interesting too, curious how it works under the hood - is it a separate LLM call or something more lightweight?

Hey PH! Sai here — built Nirixa after getting burned by invisible AI costs one too many times.

The core insight: every AI observability tool today is either provider-specific (so it can't show you cross-provider comparisons) or infra-general (so it doesn't understand LLM-specific concepts like prompt drift or hallucination risk).

Nirixa fills that gap. It's a thin SDK layer that intercepts your LLM calls and tracks:

• Token cost per feature/endpoint/user

• Prompt stability over time (semantic diff engine)

• Hallucination risk score per request

• Cross-provider latency benchmarks

We're live now. Drop your questions below — especially if you're skeptical. Those are the conversations I learn the most from. 🙏

I run multiple LLM providers in production (Gemini Flash, GPT-4o, GPT-4o-mini) across different parts of my app and tracking cost per feature has been a nightmare. Right now I'm doing it with spreadsheets and napkin math. The token cost breakdown by feature is exactly what I need. Quick question, does it work with OpenRouter or just direct provider APIs?

Nice! an all in one open claw manager. I could see this blowing up. Is there a way to track token usage for whatever LLM you plug openclaw into? A dashboard for this would be great.

Congrats on this launch! Getting AI flagged for content I actually create is such a frustrating blocked in a launch workflow. Does this work across all major platforms or are certain ones better supported than others?

Hey Product Hunt 👋

I noticed something strange while testing uploads — the same image could be handled differently after posting.

That got me curious.

After digging deeper, I realized that hidden data and encoding differences can influence how platforms process images.

So I built FeedReady.

It’s a lightweight tool that prepares your images before upload:

• Cleans unnecessary metadata

• Re-encodes for consistency

• Runs fully locally (no uploads, no storage)

This isn’t about bypassing anything — it’s about giving creators more control over how their content is handled.

You can try 2 images for free, and early users can unlock full access for ₹99 (one-time).

Would genuinely love your feedback 🙏

Excited to see InSightOS live on Product Hunt today 🚀

From a technical perspective, one of the problems we kept seeing was how fragmented financial analysis workflows still are. Teams often have data spread across multiple systems, and answering simple questions like “what actually drove this change?” requires a lot of manual investigation.

While building InSightOS, we focused heavily on designing systems that can combine structured financial data with AI-driven analysis while keeping insights explainable and traceable.

It’s been a fun and challenging engineering journey working on forecasting workflows, anomaly detection, and AI-assisted financial reasoning.

Huge credit to the engineering, product, and design teams at PhrasIQ who helped bring this together.
We’re still early and would love to hear feedback from the Product Hunt community.

Excited to see InSightOS live on Product Hunt today 🚀

Building something meaningful always takes a team effort, and I’m incredibly proud of the work everyone at PhrasIQ has put into bringing this product to life. From shaping the product vision to refining the experience and making the platform robust, it’s been a rewarding journey.

InSightOS is our step toward helping finance teams move beyond fragmented spreadsheets and slow analysis, giving them faster, clearer insights into what’s driving their numbers.

We’re still early and would genuinely love to hear feedback from the Product Hunt community. Thanks for checking it out!

Congrats on the launch @FamZam

Congrats! Just checked out your app, looks great overall.

I did run a quick audit and found a few small things you could tweak. We launched a free audit tool today, so sharing your report in case it helps.

Training and open sourcing your own model instead of just wrapping existing APIs is a bold move, and it makes the transparency promise actually credible. The "coffee price" positioning is clever too since it shifts the conversation away from feature comparisons and toward trust. @aditya_kumar_jha1 what was the hardest part of getting the model to perform well enough that you felt confident shipping it at this price point?

Super cute design. It could see this blowing up on social media.

Nice idea, i imagine there is a limited pool of phrases right? or are you generating them constantly?

Ohh this was super fun to play with, congrats on building and launching!

This is the tool I've always needed but for some reason didn't think of. Thank you. I have no idea how someone hasn't done this sooner. After GTM heavy week, it's especially wonderful to see.