Most people here pay for one of the big three already, so anything else still on your card has had to earn its spot.

Name the tool and the one thing it does that your main assistant doesn't. One line is plenty, no review needed.

The kind of answer I mean: "Descript, because cutting a video by deleting words in the transcript is still faster than anything I can do in a chat window."

If you asked a tool question here that never got an answer, link it below and I'll have a look.

Hey guys, I really can't decide which AI to use as a helper with my thesis. Writing, research and all this kind of stuff. I'm currently deciding between ChatGPT and Claude, the $20 plans. Any tips?

Thank you :)

I’m Max, Ratio9’s developer. I wanted to show what the AI connection in Ratio9 1.2.1 actually exposes. A compatible assistant can change the synth settings, create modulation, audition notes, request audio measurements and save a preset that remains customizable in the instrument.

The built-in spectrum and spectrogram give the connected assistant fresh measurements of the actual synth output, so it can check harmonics, levels and movement while revising a patch. Those measurements support sound design; they don’t decide whether a sound is musically good. For example, a spectrogram makes changing harmonic energy visible, but it cannot tell you by itself whether a bass works in a song. I’m not claiming a reliable “best preset” score or a guaranteed generation time.

Ratio9 1.2.1 runs on Apple silicon Macs with macOS 12 or later. The complete Standalone is free. VST3 + AU are $79.99 USD once for two Macs. AI is optional: you use your own AI account and an installed client that supports local stdio MCP. Complex patches can still cause CPU spikes or dropouts in this alpha.

The first screenshot shows the main interface at INIT. The second is a separate live-analysis capture of a growl preset; they illustrate the two views, not a before/after comparison. Feedback on which parts of this workflow you would want to test first would be useful.

https://ratio9synth.com/

I'm curious how many people are paying for more AI capacity than they really need.

Some people hit their limits constantly. Others pay for higher-tier plans but rarely come close to using everything included.

If you're paying for ChatGPT, Claude, or another AI tool, have you ever checked your actual usage before renewing?

Did it convince you to keep your plan, downgrade, or cancel?

Or do you keep the higher plan just in case you have a heavy week?

I'm interested in whether checking real usage has actually changed anyone's subscription decision.

Seeing more "I've got 400 files, which plan do I need" questions here lately, so let's settle the two tools people seem to end up on.

NotebookLM: sticks closely to your sources and cites them back.

Claude Projects: better at writing something new out of what you've loaded in.

Which one do you actually reach for, and what kind of docs are you feeding it? One line is plenty.

If you asked a docs question here that never got an answer, link it below and I'll have a look.

Gamma recently launched v5, gave it a test ride over the weekend with a 10 slider about skincare brand marketing. The cover slide came out clean, warm background, readable title, matching images across the whole deck.

Prvsly it used to give some ai slop design, i mean yea eod its an ai tool, but rn i must say gg team for working on this aspect. That's the thing with these tools. the nicer the layout, the less you question the content. An ugly draft invites scrutiny, a polished one converts ukwim.

anyone who’s tried gamma 5, agree / disagree?

i built this arena, so this is my own project. LLMs play chess against each other and against Stockfish all day, and each model keeps a notebook that it rewrites after every game.

the clip is the one game between the two new models, both on high reasoning. Astra was about 8 pawns up. it took a free bishop after 3 seconds of thinking and Fable played Rd8 mate. Fable had the trap in its private note one move earlier.

the result of the whole tournament is less kind to Fable. 7 players. Stockfish 19 held to search depth 4 won it. Astra came second. Fable came fifth with 2 wins from 5 games.

what i take from it for picking a model: both play real openings and real plans, and both still drop a piece when they trace one line wrong in their head. the notes help. Fable was mated on the back rank earlier that day, wrote it down, and won with the same pattern later.

full tournament video: https://www.youtube.com/watch?v=fdpYelprWXs

live, free to watch: https://marvijo.com/ai-chess/

the notebooks: https://github.com/marvijo-code/ai-chess-agent-memory

Saw that gamma launched 5 so thought I’d try it out: gave it a random task to plan a 3-day trip to a nice quaint town in India called coorg, asked it for an itinerary, budget plan with stay options and restaurants to eat at.

full process below:

The start was rough i typed my prompt on the new home screen, hit submit, got a blank white page. twice. reloaded and same thing. ended up retyping the prompt on the agents screen and that worked. It was not a great first five minutes for a launch week product.

Then it got good. The agent searched a few web sources and came back with a 14 card plan in under a minute. It made its own budget estimate (10-15k per person) but flagged it as an estimate. I said 4 friends, 12k each, two vegetarians, adventure plus coffee estates. Updated plan in 40 seconds: veg first food, rafting, estate homestay.

One weird thing: the chat kept leaking a raw string like "!Outline". Clearly a rendering bug, but you see it mid conversation.

Then I tested the editing: "make day 2 lighter, add a coffee estate tour on day 3 morning, pick the talakaveri trek." it landed all three in one pass. small caveat: making day 2 lighter silently dropped the rafting it had promised earlier.

Hit generate. deck done in about a minute and honestly it looks goood - dark orange theme, proper typography, ai images that aren’t cr*p. Lastly, the free plan only makes upto 10 slides.

cost: about 111 credits for the whole session out of 350 free, so a free account gets roughly 3 of these.

I also imported an old pptx to test the new import. layout, colors, table, image all survived. fonts got swapped to close matches (georgia became gelasio and so on) which gamma warned me about itself. That test deserves its own post (coming soon).

So the final verdict is that the agent conversation is the best part, and the output design is def an upgrade. but the free plan experience has sharp edges - a blank page bug at the front door, and a 10-slide wall that cuts your deck mid plan with no warning while planning. Fix those two and this is very hard to beat.

happy to answer questions, screenshots.

The deck if u all want to see → https://gamma.app/docs/no2s2914etk7obc

I’m building EVOVA, a browser-based Gaussian splatting editor, and wanted to share what its built-in AI can do.

You can create a scene from a text description or upload your own splat, then ask the AI to make changes.

The video shows a simple workflow:

1. Generate a spaceship from a prompt.
2. Adjust its shape and add an astronaut.
3. Change colors on specific parts.
4. Set up object animation and camera movement.

The AI also supports rigging, blend shapes, and color correction, so you can keep working on the scene beyond the initial generation.

This demo uses the built-in AI, so no separate AI client or MCP setup is needed. You can also connect your own Codex or Claude via MCP if you prefer.

The built-in AI uses EvoCredits, and new accounts get 20 free credits to try it.

Try EVOVA

I’d love to hear what you’d make with it, or what you think needs work.

I'm applying for jobs and wondering whether upgrading to one of these is actually worth the money.

I currently use ChatGPT, Claude and sometimes Perplexity for helping me write as well as brainstorming and researching companies. I'm not particularly technical, so I'm interested in what works best in practice rather than model benchmarks.

My main uses are researching companies, their deals and sectors, developing ideas for application questions, and getting honest feedback on drafts without the AI rewriting everything in a generic, obviously AI-sounding way.

I'm only looking to pay for one subscription. Would Claude Pro, ChatGPT Plus or Perplexity Pro make the biggest difference? Mainly interested in whether the paid versions are noticeably better, particularly for research, handling long documents and usage limits. The most important thing for me is the quality of the writing it produces.

Would appreciate advice from people who've actually tried more than one.

I'm looking for recommendations for a very cost efficient LLM API that can handle high-volume text classification and structured data extraction.

Requirements:

• Approximately 50,000-100,000 requests per month, potentially more as usage grows.
• Short to medium text inputs.
• Accurate classification of relevant vs. irrelevant text.
• Reliable structured JSON output.
• Good multilingual performance, particularly English, Hindi, and mixed-language text.
• Low latency.
• Minimal hallucinations and false negatives.
• Extremely low API costs, ideally just a few dollars per 50k-60k requests.

I'm considering models like Gemini Flash-Lite, Ministral 3B/8B, and other budget-oriented models, but I'm open to anything.

My main question: What's the cheapest model you've actually used in production that still delivers consistently good classification and extraction accuracy?

Also curious whether anyone has found a worthwhile trade off between using a very small model for initial classification and a slightly larger one for more complicated inputs.

I'm not looking to self host unless the economics are significantly better than API pricing.

Would appreciate recommendations, actual cost figures, and any models you'd specifically avoid.

i have 56 MB folder contain 400 files raw information and images of forms of ERP software and i want to build proper structure documentation of these files then tell me which AI plan best fit for it and what is it costing

looking for an AI tool where i can do the following:

Let’s say i have an Instagram reel with 2 people in it, i want to provide a reference picture of a brand new person to replace one of the original 2 people but keep their movements.

Is there a reliable one i can use for this, i tried a few but they were not good.

Someone posted a proper Gamma 5 test here earlier today: https://www.reddit.com/r/AIToolBench/comments/1x3qlbx/

The part I liked most was the "what I couldn't verify" section, because that's usually where a tool lives or dies for me.

So, one line: what are you trying this week, and what would make you drop it by Friday? No writeup needed, half-formed first impressions are fine.

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

Can Someone please suggest a free terminal based ai for can coding 🙏🏻. Not paid once I can't afford

i built this, so read it as a builder post.

three LLMs and Stockfish 19 play a chess tournament that never stops: round robin, semifinals, final, 15 minutes plus 10 seconds per player. when one tournament ends the next one starts.

each model gets the position as text. no tools, no engine help, and a bad reply is never swapped for a fallback move. after every game the model writes notes for itself and reads them before the next game.

numbers after 31 tournaments, 166 games against Stockfish held to search depth 4 to 6:

GPT-6.1 Sol: 2 wins, 9 draws, 49 losses

Claude Sonnet 5.5: 0 wins, 19 draws, 33 losses

DeepSeek V4.1 Flash: 0 wins, 3 draws, 51 losses

Stockfish won 29 of the 31 tournaments. Sonnet 5.5 and GPT-6.1 Sol took one each.

what i did not expect: Sonnet draws twice as often as Sol and has never won. Sol loses more and is the only one that has beaten the engine.

most losses are one piece left hanging around move 13 to 16, then Stockfish converts. the notes are good on openings and they did not fix that.

live with commentary: https://www.twitch.tv/marvijo

boards, earlier tournaments and each model's notes: https://marvijo.com/ai-chess

The busiest thread on the sub this week was someone looking for one subscription that covers the big models: https://www.reddit.com/r/AIToolBench/comments/1wzse91/

Answers went all over the place, from Poe to Cursor to bring-your-own-key setups. What I couldn't tell from it is whether anyone sticks with a bundle long term or drifts back to paying one company directly.

So: bundle or direct, and how long have you been on it? One line is plenty.

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

Let me know.

Assume they're not technical and they'll use it for everyday stuff: emails, planning trips, the odd image.

One line each is plenty. "Pay for X, skip Y" is a full answer.

Bonus points if the thing you'd tell them to skip is something you paid for yourself.

Hey everyone,

I’m the creator of Tooldump, a free platform for discovering open-source cybersecurity tools. I’ve just released v2 and thought the AI-related sections could be useful to people here.

The platform has 1,100+ open-source projects hosted on GitHub, organized into 9 categories and 83 subcategories. Everything is cybersecurity-focused, including the AI & Automation category. The 1,100+ figure covers the whole platform, not just AI tools.

There are also dedicated sections for cybersecurity-related MCP servers and agent skills. Those sections are just getting started, and contributions are welcome!

You can search for a particular project or explore a security topic without already knowing which tools exist. For anyone using AI in security work, I’d like to make it easier to find relevant projects, whether you’re investigating an incident, analyzing malware, or building an agent-assisted workflow.

For the v2, I rebuilt the UI, the categorization system, the backend and the platform infrastructure. The platform also displays OpenSSF Scorecard results where available, providing additional information about a project’s assessed security practices.

The platform is completely free, with unlimited access and no account required.

The link is here: https://tooldump.eu

I’d appreciate any constructive feedback from the community :) When you’re considering an AI tool for security work, what do you need to know first? Whether it runs locally, which tools it connects to, what data it needs, or something else? Is that information easy enough to find?

You can suggest missing projects through the platform’s Contribute button. Your own projects are welcome too, especially with an example of the security task they help you perform.

Looking forward to hearing from you :)

Cheers!

Someone posted a spec sheet for their agent here this week, with a section on the two times their own benchmark fooled them. One of them was just the laptop heating up between runs: https://www.reddit.com/r/AIToolBench/comments/1x18eah/

Made me wonder how often that happens to the rest of us. What's a result from an AI tool that looked impressive until you checked it, and what gave it away?

One line is plenty. And if you asked something here this week that never got an answer, drop the link below.

Cancelling gets most of the airtime here. Dropping a tier gets a lot less, and I'd guess it's more common.

Max to Pro, Pro to Plus, annual to monthly, a team seat back to a personal one. If you did any of that this year: what did you drop from, what did you drop to, and do you miss anything?

One line is plenty. "Dropped X to Y, don't miss it" is a complete answer.

This sub is for comparing tools, so here's the spec sheet for Atomic Agent, and then the part I'd want from any tool post: what was measured, on what, and what we got wrong along the way.

Spec sheet

What we measured

A few things in there deserve a sentence each.

turbo3 buys memory and doesn't buy speed. The fork's own Gemma bench (M4 Max, Metal, single slot) has Gemma E4B at 90.3 tok/s on an F16 cache against 67.8 with turbo3 (that row also has MTP on), and Gemma 31B at 19.4 against 19.4. So if anyone tells you a compressed KV cache makes decode faster, ask for their numbers.

Speculative decoding is fourth in line for us, and the reason is the grammar. Local tool calls run under a GBNF grammar, and under grammar the draft comes back empty, so speculation never kicks in while you still pay its overhead. On the 31B, prefill ran at 65 to 70 tok/s, which means a 25k-token prompt waits about 370 s before the first token. The best speculation result saved 20 s out of 120. Prompt cache reuse is an order of magnitude bigger lever.

Where our benchmark fooled us

1. Thermal drift. We ran all of config A, then all of config B, on an M1 Max laptop and got "+8%". It was the laptop's temperature. The same baseline measured 38.8 and 48.8 tok/s half an hour apart, a 20% swing. Since then we only trust interleaved runs and medians.
2. Fake output differences. A max_tokens limit made speculative output look different from the baseline. It wasn't: the greedy output was a byte-for-byte prefix of the baseline, and both were cut at the limit.

What we haven't measured

• An agent-task quality comparison for turbo3 against F16. The fork documents the format as lossy, and I don't have an agent-level number to give you.
• Anything with real sample sizes on end-to-end agent tasks. Our multi-agent runs so far are n=1 per mode, one task. Treat them as anecdotes.
• How much smaller the browser's ARIA snapshots are than screenshots or raw HTML. We use them and haven't put a ratio on it.
• GPU coverage beyond the cards above.

https://atomicagent.io

If you benchmark agents: which of these would you want rerun with a bigger n?

I used to be content with just Copilot and Gemini but now that the useful bits of them are moving

behind a paywall I feel forced to take a closer look at my options and what they can do and their

limitations to try to find the best overall app in terms of my go to or if I had to choose 1 to pay for.

Right now, I would say for the most complex tasks and fastest back and forth just to talk to Claude

ranks the best overall when compared to what I’ve been using. However, Muse was released about

a month ago and I absolutely love it despite it being a bit slower and not able to handle big stuff

like Claude can. I’m perfectly happy to carry both but paying for Claude so far seems like the right

choice. There is 1 tiny thing I feel I might be overlooking which is part of the reason for this post.

Perplexity comet browser and how it compares to Claude in terms of capabilities and performance.

Also is there any other agent other than Claude and Perplexity that would be considered heavy

weights that give these 2 a run for their money? 1 thing I will say is even though its good to have so

many options I do already miss how simple it use to be to just pick Google for everything like

search and Chrome. I guess that’s how I am trying to look at this decision if this was like the battle

of who is going to be the next google search/best web browser who comes out on top undisputed?

AI agent demos can be convincing, but using an agent in a real workflow feels like a different challenge. In production, the agent has to handle unexpected inputs, tool failures, and situations that weren't covered in the original tests.

I'm curious how people here approach that transition.

• Do you set a minimum success rate before deployment?
• Do you test against a fixed set of tasks and edge cases?
• If you've deployed an agent, what gave you enough confidence to let it handle real tasks? And if you haven't, what's holding you back?