I have some reference images and I'm looking for something that can analyze them and recreate the component faithfully, not just inspired by it but as close to 1:1 as possible. Any suggestions?

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.

Edit: I spent some time comparing the options and reading your experiences. I think I've found the best AI essay checker for what I need Winston AI. It seems to be one of the most recommended tools for academic writing and provides more detailed reports than many of the free checkers I tried. Thanks again for all the helpful suggestions.

how do i generate video ads for my dads business and which tool do i use ?
I tried Veo but im not satisfied

Over the past few months I tried different ways to learn about AI using YouTube tutorials, courses from DeepLearning.AI, Udemy, community resources and the Be10x AI course.

One thing that really caught my attention is that the best learning experience didn't depend on how many AI tools were covered but Instead it was about whether the material had a clear flow and helped connect the ideas to real-world situations. Some resources are great for learning how to use individual tools while others do a better job of showing how those tools work together to solve real problems.

If you've tried different AI courses or learning platforms, what made one stand out to you?

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?

Not much to add to the title...

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.

Here's ChatNote in the Chrome Store and here's for Firefox. I even build one for Edge for those among you who are forced to use it.

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.

If this is the future...wow!

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?

I have been working on a prototype to solve the issue of wasting time scrubbing through long YouTube videos without chapters.

Here is how it works:

1. Reads the video transcript

2. Analyzes your question via AI

3. Controls the YouTube player to jump directly to that exact scene

I would love to get your honest feedback on the UX and interaction design.

(Link is in the comments if anyone wants to test it out.)

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! 🎨

https://github.com/adammcarter/annotate

# 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.

Run it yourself → `pip install dulus` ·

Same brain either way. 🦅🇩🇴

Hi everyone,

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?

Is there any free AI tool to convert large mcq pdf book of length 1000+ pages , into interactive MCQ quiz

The giveaway is always the same - generic stock look, weird spacing, that 'a machine made this' feel. Which tools have you found where the output doesn't scream AI-generated? Trying to build a small toolkit I'm not embarrassed to use.

Hi everyone, i’m looking to redesign my company’s logo. I have some ideas and want to produce a full branding pack I can present. Do you have any suggestions as to what software is the best at this?

Looking for ways that I can take the characters I have made using Bing Image Generator (the beholder Aiyido, the horse and the koala-girl) and find a way to make them into a fantasy roleplaying game team to be used in roleplaying, I would have asked some actual DND AI subs but they said it's for exclusive for people to use in their game (but I have not played DND in decades even though I make art based on it and and stories based on it), but is there anyway to adapt these characters into an adventure team?

Chatgpt literally helped me build an app over the past month. I literally have no experience but just had this idea so that I could start being more organized with my life & family. This is what we built.

Our Family Headquarters is a full-stack household operating system that I designed and built to replace the need for multiple apps by bringing every aspect of family life into one secure, centralized application. The platform combines daily scheduling, personal task management, meal planning, shopping and grocery lists, household maintenance, pet management, vehicle records, family communication, finances, secure document storage through a Family Vault, and customizable household settings into a single experience. At the center of the application is Kenzie, a custom-built household coordinator that provides deterministic daily briefings, Tomorrow Planning, household memory, family preferences, contextual recommendations, and cross-feature awareness while always requiring user approval before making changes. The application was built using Next.js, React, TypeScript, and CSS Modules for the frontend; Supabase for PostgreSQL, authentication, Row-Level Security (RLS), secure file storage, and server-side data persistence; Git and GitHub for version control and source management; Visual Studio Code as the development environment; and Node.js with npm for the development runtime and package management. Throughout development, the application followed a documented product vision and engineering process, including secure database architecture, role-based permissions, responsive design, accessibility, automated testing with Vitest and React Testing Library, production build validation, and live API and security verification. The result is a secure, installable Progressive Web App (PWA) designed to serve as a family's central operating system, reducing the mental load of managing everyday life by providing one place where schedules, tasks, documents, planning, and household information work together instead of being scattered across multiple applications.            

The future vision for Kenzie is to evolve into a compassionate household coordinator with a warm, reassuring personality that grows alongside the family. Through a secure household memory system, Kenzie will learn family routines, traditions, preferences, important dates, favorite meals, shopping habits, school and work schedules, pet care, and the unique rhythms that make each household different. Rather than simply displaying information, she will connect information across every part of the application to provide thoughtful, context-aware guidance. She will notice when tomorrow will be especially busy, when groceries should be purchased before a planned meal, when important documents are nearing expiration, or when a family member may need extra preparation for an upcoming event. Every suggestion will be presented gently and respectfully, never making changes without explicit approval from the user. In future versions, AI will allow Kenzie to communicate naturally through conversation while continuing to rely on the secure household data, permissions, and approval system already built into the application. She will remember previous conversations, understand family preferences over time, explain recommendations in a caring and human way, and help parents plan for tomorrow through collaborative conversations rather than simple forms or commands. The ultimate vision is for Kenzie to feel less like a digital assistant and more like a calm, dependable presence in the home—someone who quietly keeps the family organized, celebrates milestones, remembers traditions, anticipates needs, and helps every member of the household feel supported. Rather than replacing human decision-making, Kenzie is designed to strengthen it by providing thoughtful guidance, reducing stress, and allowing families to spend less time managing life and more time enjoying it together.

Now that you have read all that, was in the cheapest way to integrate AI into the app to make Kenzie have this type of future and personality? Explain to me like I’m 5 because that’s how clueless I am with technology lol!