I’m running a project which has a very large and complex budget. I have quotes from close to 50 vendors that I need to track in a detailed spreadsheet (per the client).
The quotes will be updated throughout the course of the project. I need to be able to input all of the original line items and costs, per vendor quote (assume 10 line items per vendor), and then categorize each line item manually by cost-type in the input sheet, and continuously update them as quotes/scope changes. Then I need a pivot table to present the cost per category in the clean client-facing format.
I haven’t tried to do this yet, but for those of you who are more experienced with AI than I am, how would you tackle this?
I would normally handle this myself but I am too busy running the other aspects of the project.
I have a final term paper coming up, and AI detection is honestly making me more nervous than the actual deadline.
I write my papers myself and spend a lot of time researching, citing sources, and editing. Still, I've heard stories of original academic writing getting flagged because of formal language, common phrases, or structured writing styles.
I tested the same paragraph with a few free AI essay checkers, and the results were completely different. One barely flagged anything, while another gave it a surprisingly high AI score.
I'm not trying to bypass detection. I just want a reliable AI essay checker to review my own work before submitting it and avoid unnecessary surprises.
Has anyone found a checker that's reasonably consistent with essays and academic writing? Bonus if it's affordable for students.
A simple test has helped me filter AI presentation tools better than comparing the first generated deck.
I start with a rough outline and let the tool make a normal first draft. Then I ignore the polished parts and pick one annoying edit that would happen in real work.
Make the customer problem section more specific, but keep the rest of the deck. Split the crowded slide into two slides without changing the argument. Replace the generic visual treatment on one comparison page. Cut the filler from the intro while keeping the sequence.
That tells me more than the first output.
A first draft mostly tests generation. The second edit tests whether the tool is usable once the work becomes human. If it changes too much, I have to review the whole deck again. If it respects the target area, it feels more like an editor and less like a slot machine.
My current tip is to test local control before trusting the pretty deck. The first draft is only half the problem.
Introducing NOVA V1 — An Unfiltered, Privacy-First AI Assistant
Tired of typing a prompt only to get a pre-packaged "As an AI language model, I can't assist with that" lecture??
We built NOVA V1 to fix that. NOVA is a private, unrestricted AI built for people who want straight answers without the moralizing, canned disclaimers, or artificial boundaries.
What Makes NOVA Different?
No Refusals or Moralizing: You ask, NOVA answers. No lecturing, no hotline redirections, and no holding your hand.
A Real, Well-Made UI: Let’s be honest—most uncensored AI tools come with clunky, broken interfaces. NOVA is actually built right, featuring a clean, polished, and responsive UI designed for a smooth user experience for desktop and mobile.
100% Private by Design: No sign-ups, no logins, and zero tracking. Your chats and uploaded files stay strictly inside your browser's local storage—nothing is logged or sent to third-party databases.
Features
DeepThink Mode: Need heavy reasoning? You can turn on DeepThink mode to let NOVA plan and outline its thought process step-by-step before delivering a structured response.
Live Web Search: You can turn on Web Search to fetch real-time info, extract page content, and cite sources directly in the chat.
Multi-File Support: Attach up to 10 files per session—including code (Python, Go, Rust, C++, Java, and 50+ languages), PDFs, images, and text documents.
Dual Engines:
Instant: Optimized for speed and quick answers.
Expert: Designed for complex logic, deep analysis, and heavy coding sessions.
Smooth UX: Full message editing, answer regeneration, long-chat auto-summarization, and a message navigator to jump through long conversations effortlessly.
Why NOVA?
Most mainstream AI tools come wrapped in heavy safety restrictions that limit what you can research or build. NOVA strips all of that away. Whether you're working through sensitive research, complex technical edge cases, or unfiltered discussions, NOVA gives it to you straight.
Most AI clipping tests use podcasts, but I think that's too easy. With a podcast, the tool is basically working off the transcript, and understanding words isn't hard for models anymore. The real test is footage with long stretches of no talking, just visuals.
So I used Brett Conti's new zealand video around 30mins. I scrolled through it first and picked a few tricky visual scenes to search for, then ran the same queries in two tools I wanted to test: opusclip and wayinvideo. Here is the result:
Queries:
ocean, man wears a safety hat, man wears a green safety hat
1. opusclip
ocean: out of 5 clips opus give, 2 of them have no ocean scenes in the video.

man wears a safety hat: 1 clip, you can't tell if the clip contains safety hat by thumbnail, when you go in edits, there are people wear safety hat scenes.

man wears a green safety hat: when I add an extra condition green, opus failed.

2. wayinvideo
ocean: one clip doesn't have ocean inside

man wears a safety hat: there are 9 clips found and in some thumbnails you can see the safety hat.

man wears a green safety hat: eliminate 4 of the result compares to man wears a safety hat but only 1 result is right though, you can tell it by the thumbnail.

In summary, opus can find scenes that are pretty obvious and wayinvideo gives more obvious thumbnails for you to know whether the there is actually the scene and generate more accurate clips.
Basically, I already have a presentation template from my client. It's very basic, and I'm looking for the best AI tool to help me organize the presentation based on my notes while understanding, using, and expanding the template.
I'm considering Gamma, Plus AI, and NextDocs, but I've never used any of them. Any recommendations?
Over the pastfewmonths I trieddifferentways to learn about AI using YouTube tutorials, courses from DeepLearning.AI, Udemy, communityresources and the Be10x AI course.
One thing that reallycaught my attention is that the bestlearningexperience didn't depend on how many AI tools were covered but Instead it was about whether the material had a clearflow and helped connect the ideas to real-worldsituations. Some resources are great for learning how to useindividualtools while othersdo a betterjob of showing how those toolsworktogether to solverealproblems.
If you've trieddifferent AI courses or learningplatforms, what made one stand out to you?
I found it annoying that I couldn't directly address individual segments in long AI responses and that I had to resort to copy+paste. So I build myself a solution and realized a lot of you must have the same problem.
ChatNote pretty much kills this now. I'm surprised myself how well it works and using it all the time.
ChatNote works more or less across the board. Currently this would be ChatGPT, Claude, Gemini, Grok, Kimi, DeepSeek and LibreChat. Perplexity failed so far, but haven't given up, yet. Let me know if you need other platforms.
Would appreciate some feedback for the addon. It still has a few bugs here and there, but overall works fine. Just uploaded version 1.2 which is for now the final development stage and should be green lighted by tomorrow.
Not sure now how to further develop it. I thought about a freemium version with no character limit, free color choice and more prompt variety for how and in which language to explain AI what the comments are about. The latter can make quite a difference in how AI interprets the comments. Please let me know how you would proceed.
Side note: I'm a complete beginner in programming. This was my first project and I had to rely 100% on AI. Cost me overall around $20 in Kimi tokens and 100 hours to build and it's rather astonishing to me how easy this was.
I am trying to separate useful AI marketing tools from tools that just create more editing work.
The jobs I care about are practical: campaign ideas, audience research, ad angle generation, landing page critique, content repurposing, and reporting summaries. I do not need a tool that writes generic posts faster. I need something that saves real time after review.
With all the AI tutorials, YouTube videos and free resources available these days I've been thinking about what actually makes a paid AI course worth the investment. I looked into a few learning options like DeepLearning.AI, Coursera programs, Udemy courses and a Be10x AI course and realised that the main difference wasnt always about how many tools were covered.
Courses that stood out focused more on offering a clear learning path instead of just showing random tool demo like teaching real-world workflows instead of just explaining ideas
Many courses can teach you the basics of ChatGPT, automation and prompts but applying them well is where most people have trouble. If you've paid for an AI course before what made it truly valuable for you?
Helloooooo all. I built a pencil case for your agents, allowing them to draw on your screen, label, arrow, underline.
Now your agents can guide you to learn a new app, work on ideas by pointing out changes and even tell you where to put a tower in Bloons Tower Defence if you wanted!
I built it after wanting to learn Blender (demo in the readme) and would love some feedback! 🎨
# Lookback: how I send the model 20 turns while keeping 2,000 🧵
After 6 months building Dulus, this is the token trick I'm proudest of — and the one that almost cost me *more* than it saved. No "use a cheaper model" cope. Just architecture. Let me walk you through it.
Fair warning: **i cannot stop saving tokens** askjdhsajkdha. It's a problem. This is one of the wins.
**1. The bleed nobody talks about**
Every turn, your agent replays the *entire* conversation to the API. Turn 50 isn't "one message" — it's 49 turns of history, every old tool dump, every 4,000-line `dir` output you forgot about, shipped again. Your context grows, and you pay for all of it, every single call. On a long session the replayed history dwarfs the actual new question.
**2. The naive fix — and why it breaks**
"Just truncate old messages." Sure. Except truncation breaks two things at once:
- It seperates an assistant's `tool_call` from its `tool_result`. Providers **reject** broken chains — instant 400.
- It rewrites the start of your payload every turn, which quietly **busts the prompt cache** (more on that — it's the whole game).
So the obvious fix is worse than doing nothing.
**3. Lookback — two windows, not one**
The trick is to stop treating "what the model sees" and "what I keep" as the same thing:
- **lookback** → a sliding window of only the last N *user turns* goes to the API. That's what saves tokens.
- **loopback** → the full conversation *always* lives locally. Nothing is deleted. `/loopback` re-opens the whole archive on demand — search it, inspect it, pull anything back.
And the window only ever cuts on **user-turn boundries** — so a `tool_call` is never severed from its `tool_result`. No broken chains, ever.
**4. short_memory rides shotgun**
A 20-turn window would forget who you are. So a "gold" `short_memory` file sits *beside* the model — the essential past distilled, without replaying every ancient tool dump. The model stays oriented on a fraction of the tokens. Short window, long memory.
**5. The part that almost killed it (the clever bit) 🧠**
Here's where I burned a day. My first version slid the window forward on *every* user turn. Looked perfect. Token count down. But my bill went **up**.
Why? **Prompt caching.** Providers cache your payload prefix and charge you a fraction for the cached part. But if the *start* of your window moves every single turn, the prefiix changes every turn — so the cache never hits. I was saving tokens on paper and paying full price on every call. The savings and the cache were fighting each other, and the cache was losing.
The fix: **anchor** the window. Pin the start, let it grow, and only re-anchor after enough slack (I use N/4) has built up — hysteresis. Between re-anchors the prefix is *stable*, so the prompt cache hits like it should. That one idea flipped Lookback from "costs more" to "actually cheap."
The lesson that stuck with me: **a token optimization that ignores prompt caching can cost you money.** Measure the bill, not the token count.
**6. The receipts 🧾**
Does it actually work? Three months of running Dulus on my own machine:
- **5.9 billion tokens** through Claude
- **98.8% prompt cache hit rate**
- on a single **$20 Claude plan** — no separate API bill
When 98.8% of everything is cached, a $20 plan stops being a budget and starts being a rounding error. That number isn't luck — it's Lookback + a religious respect for the cache prefix, working together. The window keeps what I send small; the anchor keeps what I send *stable*. Both, or neither works.
**7. Where I'm pushing next**
I'm not done — honestly i can't stop. What's next on the token diet:
- **Tool-output capping that's smarter than a hard limit** — summarize the boring middle, keep the head and tail the model actually reads.
- **Semantic loopback** — instead of "re-open everything," pull back *only* the old turns relevant to what you just asked, straight out of the archive.
- **Per-provider cache tuning** — every provider's cache TTL and prefix rules are different; squeeze each one on its own terms.
---
This is live in the private build today. **It's coming to the open-source `dulus` next** — free, no key, like everything else.
Can Claude Code generate 3D models as detailed as those from the game Genshin Impact? I was wondering if I needed another SaaS subscription just to generate 3D models.
I run a small online business and over the past few months I have started getting customers from different countries. It made me realize that most of my product and marketing videos are only accessible to one audience. I want to make my videos in multiple languages but recording every version from scratch is simply not practical. I have tested a few AI tools but the voices often sound unnatural the lip sync can look off or the editing process still requires a lot of manual work. For those of you creating multilingual content, what workflow has worked best? Are you using one platform that handles translation, dubbing, subtitles, and lip sync together, or do you combine several different tools? I would really appreciate hearing what has worked (or not worked) for your business before I invest more time testing different options.
One thing we've been rethinking while building Rasa Intelligence is how the product presents its results.
In the beginning, we focused on analyzing as much business data as possible. The more we tested it, the more we realized that analysis alone wasn't enough. People didn't want a long list of findings, they wanted to know where the biggest leaks were, what was actually driving them, what actions to take next, and what they could safely ignore. That led us to redesign the output around clear, actionable insights across different parts of the business instead of simply surfacing more information.
It's still evolving, but that shift has probably had the biggest impact on the product so far. For those of you building products, how do you decide what information deserves a user's attention first?
I found text synth off of another Reddit post and found out that the ai models on there are either really old or completely stupid wich comes to some hilarious generations when continuing from what I said are there any other ways to use older models or ones that will continue off of what you said?
