SlidesAI

SlidesAI AI

SlidesAI AI

SlidesAI AI

SlidesAI AI

SlidesAI AI

SlidesAI AI

SlidesAI AI

SlidesAI AI

SlidesAI AI

SlidesAI AI

Inter-1 does streaming: real-time social signal detection from live video, audio & text

Hi – Filip from Interhuman AI here 👋 Last month we launched Inter-1, our multimodal model for detecting social signals from video, audio, and text. Today we’re making it work with video streams. We just released the Inter-1 Streaming API: a WebSocket endpoint that runs the full Inter-1 stack - 12 social signals, structured rationales, engagement, and conversation quality on live video while the conversation is unfolding. You stream WebM chunks in, and get back regular updates with detected signals. The model runs in sliding 8s windows with a sub-1.0 processing ratio, so it’s fast enough to power live coaching prompts, in-call overlays, and adaptive UI. It’s not meant to be a full voice agent on its own, it’s the behavioral signal layer you plug under whatever interaction system you’re building. If you’re working on sales/CS tooling, interview coaching, training, or live feedback products and want to experiment with real-time social intelligence, it might be worth looking into. Happy to answer questions or brainstorm use cases in the comments. submitted by /u/Sardzoski [link] [comments]

Plus 5 hr usage limits

Not sure if OpenAI monitors this channel. I've been a chatgpt and codex user for a long time. My preferred codex model is gpt-5.3-codex, but this is primarily because the 5hr usage window of gpt-5.5 effectively makes it useless. This was not always the case. In fact in general I've used codex less because there's been noticeably less usage. For context I've switched things up and can dynamically route to any model mid context (took 6 months to build and test) mainly to have the freedom and flexibility I have now The point of me writing this is not to have a whinge but to share developer feedback. At one point your usage limit restrictions had me considering moving to a Pro plan. What I did instead was build a token solver that maintains context and tool awareness and can interdict a call to any llm and finish a prompt, effectively giving me no rate limit on any task. Because I have failover built into it, as well as a heuristic intent model, it can hit a rate usage on openai then preserve context and fallback to gemini flash then fallback to ollama cloud. I paid $200A a year for ollama cloud and I pay about $30A a month for gemini pro and $30A a month for plus. I guess a I'm saying I would have paid you the $150A a month if I didn't have faith you would just throttle the 5x plan so I effectively eliminated the need for it for $80A a month. In otherwords your plus usage is too low by 2x. Interestingly a few months ago you did have 2x usage, and I never needed my fallback system. I guess a I'm here to validate 2x for plus is the sweet spot. $150 won't add value if you keep sliding the throttle. To anyone still reading I will be putting my solution on github. My current rig requires Linux but I'm going to do a docker and openclaw build and stablize before I push publically. submitted by /u/SimulationHost [link] [comments]

A plugin that slows you down on purpose

Hi all. Out of respect to other humans this is written by a human. You all should take an Uber to get to the carwash. My name is Ilya and I want to share my ecosystem of skills and agents (and a couple of rules + hooks) that I've built for myself over the past 5 months because I wasn't happy with anything that the market currently offers. I use it on daily basis, and it only contains stuff that I needed to solve problems I faced, and I'm super happy with how it works. Quick context: currently I work in strategy consulting. But I got lucky enough to get consistent exposure to managing people for over 20 years. Running my own business, turning around others' businesses, playing colony management games, managing consulting teams, and most importantly - managing a mid-sized guild in an MMO (if you've done this you know). I am not a software engineer, although I do code a bit. The main idea was to organise AI in a way I would organise a team of very capable people. So this is mostly for thinking work, including coding, not just for coding. --- Why slow AI gives us speed. It's good, but the flip side - it's bad in some situations, and I see that many people miss it entirely. AI is great at following directions. If the direction is wrong because you rushed it, the wrong thing gets executed very quickly. The fix is unsexy and requires patience: spend time on the brief upfront, make the AI push back when something doesn't make sense, then check what came out before stacking the next step on top. Feels slower, is slower at first. But you end up with what you actually wanted instead of another slop-fest, so it's net faster eventually. --- The 7 principles I've built this on Slow is fast - to own the understanding you can't rush Bad communication kills results (human-to-human, human-to-AI, and human-to-self - we're often misleading ourselves thinking that we know what we want) We don't know what we don't know - AI must help you to see outside of your bubble Any computer task is doable by AI if AI is properly organised - tasks are small enough, well defined, and well assessed Solve for problems that exist now, not theoretical or aspirational ones, to stay focused (and save tokens) Context is king - shit in, shit out AI can help you deal with AI - especially by doing the boring organisational work for you --- Two examples of how it works to start with /shaping - my most-used skill. It's a small workflow where orchestrator uses 3 underlying skills in a dialogue mode and helps me to frame the problem depending on where I am in my understanding of it. It solves multiple problems - more often than desired, I think I know what the problem is, but in reality the problem is somewhere else. Often, it helps me to find a better (and simpler!) solution. This is somewhat similar to why companies pay for consulting - because they know that finding the right question is 90% of the answer. This is, as you guessed, slow - but it helps to improve defining the direction for work. Which is a big deal in management, including managing AI. /critic - this is when it comes to comparing what was produced to what was intended. It invokes a subagent, that is taught to assess the quality of stuff produced. It then gives an actionable unbiased feedback. Obviously, if the direction was wrong, there won't be much value in it, but when the direction is right - it does miracles for me. Works best for non-code artefacts (PRD, architecture, skills, slides, written documents). Together they bracket the work - shaping at the start to figure out what's actually being asked, critic at the end to check the output matches it. --- What's in it Four plugins (title is a bit misleading for controversy, sorry), MIT. Each works alone, but they compose: - rageatc-core - thinking infrastructure. Ideation, understanding, solutioning, briefing, research, producer-critic-learner loops, writing skills, persuading. The most-used plugin. - rageatc-tech (small one) - a bit of extra tools the agent can reach: browse, PDFs, with fallbacks when primary tools aren't available. - rageatc-code - software building the slow way. An improved version of Superpowers by Jesse Vincent embedded in my workflow. TDD enforced, architecture before code, scale-adaptive. Heavy on persistent project knowledge - PRD, architecture, roadmap, orchestration plan. - rageatc-design - design systems for UI work. Greenfield or extracted from existing code. This is an amazing interface-design by Damola Akinleye embedded in my workflow. Most software work uses all four. Non-coding work usually only needs core and tech. --- vs Superpowers rageatc-code draws heavily from Superpowers by Jesse Vincent - TDD enforcement, worktree isolation, verification discipline. What rageatc-code adds on top: persistent project knowledge (PRD, architecture, roadmap that survive sessions), scale-adaptive workflow (matches rigour to project size), and tight integration with rageatc-core'

the gamma connector + claude projects is the investor update workflow i wish i had 18 months ago.

run a saas for indian tutors. $12K mrr. send monthly investor updates. used to dread the process. assemble data from 4 sources, write the narrative, format a deck, send. current workflow using claude projects + gamma connector: step 1: my "investor relations" project in claude has all my previous updates, investor preferences, and financial data format. no context-setting needed. step 2: paste this month's numbers into the conversation. ask claude to draft the update in the format investors preferred last time. claude already knows the format because the previous updates are in the project knowledge. step 3: trigger gamma connector. claude sends the narrative to gamma. gamma generates a 4-slide visual deck. i review in gamma's editor. minor adjustments. step 4: send the gamma link in a short email. total time: about 12 minutes. down from the 25 minutes i was spending 6 months ago, which was already down from the 3 hours i was spending a year ago before using any AI. the compound effect: each month's update is better than the last because claude references previous updates and my investors' feedback patterns. the third time the system generates an update, the output already anticipates what questions the investors will ask based on the data trends. investor response rate on the new workflow: above 70%. on the old google doc format it was 0% for over a year. the integration between projects (persistent context) and connectors (output to external tools) is the thing that makes claude feel like an operating system instead of a chatbot. for anyone doing regular reporting or updates: the project + connector combination is worth setting up. the setup takes 30 minutes. the monthly time savings compound. submitted by /u/Unique-Affect-6135 [link] [comments]

I ran 100 Claude + Codex sessions in parallel to understand what I'm doing wrong in marketing my open source "Claude Command Center". Here's the playbook they came up with.

A week ago I launched my open-source project (Claude Control Center) on this subreddit. Got 0 upvotes. Dead in 5 hours. :) [The app is awesome - great way to manage multiple sessions and avoid waiting on top of Claude + Codex - try it :) git:amirfish1/ccc . So I spawned 100 Claude + Codex agents in parallel and asked them to figure out what I did wrong (It had two hours left on my weekly Claude limit and 20% left - tried to think of good use :) ) . 30 minutes and 100 artifacts later, they handed me back a playbook. https://reddit.com/link/1tfbxmf/video/0mi1ytksol1h1/player The headline finding: stars don't come from better code. They come from: marketing surface. Tagline, demo GIF, founder credential, hosted landing page, multi-shot Hacker News, awesome-list inclusion. The system found that gap on its own - I never told it to study marketing. 5-min video walking through the 7 findings + what the agents drafted (Show HN body, X thread, LinkedIn post, channel plan): https://youtu.be/Tm2svTe_Ed4 The video itself - is *ON PURPOSE* 100% built by the AI who created the agents [happy to share the skill that builds it]. I brought: - Becky (the narrator) is ElevenLabs Jessica (TTS). - Lip-sync is fal.ai OmniHuman. - Playwright for screenshots. - Slides are HTML rendered via Chrome headless. The whole make_video.py pipeline + the 100-agent spawn script is open if anyone wants it. The interesting thing isn't the video - it's that 100 parallel agents found a non-obvious channel (Anthropic's official plugin registry, which nobody is using) that I would never have spotted myself. https://preview.redd.it/mwvi8t9arl1h1.png?width=3588&format=png&auto=webp&s=ffd8130b52330ffd1470d59c23d656cc29c24b65 https://preview.redd.it/r0w1rnvgrl1h1.png?width=3588&format=png&auto=webp&s=bf086423552102b82fe4dd5931243329bf1c61d0 https://preview.redd.it/tlyv7bgcsl1h1.png?width=2784&format=png&auto=webp&s=08d5810f14f4b3237825f7116fe965483ef0ffdd Happy to share any of the prompts, the scripts, or the marketing package that was generated. submitted by /u/Mediocre-Thing7641 [link] [comments]

Sharing all KGC 2026 decks. More production-grade KG systems than I've seen at any conference. [D]

Didn't make it to New York for the Knowledge Graph Conference this year, but caught some talks virtually and managed to download all the decks. Sharing them below because some of what was shown is worth knowing about. Majority of the presentations described live production systems. Enterprises showing up with real engineers delivering real compliance requirements. That's not usual for most ai eventss. Most talks are proofs of concept with a "coming soon to prod" slide at the end. For eg - Bloomberg showed a formal dependency model for ontology governance. AbbVie walked through ARCH, their internal KG for drug and disease-area intelligence, connected to a scoring engine, a researcher dashboard, and an LLM companion for plain-language queries. The KG is the source of truth. The LLM is the interface. Even Morgan Stanley showed continuous SHACL drift detection on risk reporting data - automated weekly checks that alert when the semantic layer deviates from what's governed. Crux: knowledge graphs are being actively used as infrastructure, not a retrieval layer on top of vectors. The graph is doing reasoning work, not lookup work. We've been skeptical of the "only using vector dbs" framing for a while. These production systems are the clearest evidence I've seen of where that breaks down - and what the alternative actually looks like when it's running. Link to the all the decks in the comment. All decks here: https://drive.google.com/drive/folders/1Csdv4hZePrBMJGggsisPXYBueTRCK1kV?usp=sharing submitted by /u/Ok_Gas7672 [link] [comments]

Claude Artifacts basically killed Google Slides and Powerpoint

Here is how I did it: Claude has access to my context + Codebase It has browser access to my branding (either through codebase or through brandfetch) It knows about popular libraries for the design components So all I had to do it to prompt it to create a general presentation about [insert-topic] (in my case it was AI Agents, but it could be about baking bread or whatever project you work on) Then I published it as a Claude artifact. Result: a sleek presentation that is 100% relevant to my context. Still some AI-generated text I had to rework but globally the experience is 300000% better than creating slides from scratch. You can remix it easily and make it your own. Everything is vanilla html/js/css, so easy for Claude to work with! Would love to have your thoughts on it submitted by /u/quang-vybe [link] [comments]

Where I'm at with AI Assisted Building + Current and Future Workflow Overview

I've been in an AI dive bomb for probably a couple of years now. The early days... when models couldn't be trusted for more than 5% of the code you wrote. Over the last 2 years that's evolved so quickly that I now write nearly 0% of my code by hand, on personal projects and at work. I've used all kinds of tools in that time too. OpenCode, Zed, Claude Code, Codex, Cursor, Windsurf, OpenCLAW, Lovable... and probably a bunch more I can't recall in the haze that's been AI ADHD for me. Over that time, I started with just copy-pasting code between ChatGPT's interface and my IDE almost like a slightly faster Stack Overflow search. Then that somewhat evolved with Cursor quite a bit. I sort of went from prompt engineering to something closer to a human relay pattern. Then, with Plan Mode becoming a thing, I think I naturally gravitated more towards planning everything because planning felt so cheap. Originally, I used to think that architectural discussion and planning was something that was reserved for larger features, but with expediting my ability to do research, orient myself within a codebase, and know what tools I have to reach for doing technical specifications for everything felt reasonable. From the human relay pattern, I started evolving into more autonomy, especially when Claude Code came out earlier last year. Between the combination of Cursor and Claude Code, starting to get orchestration, starting to use skills more heavily, starting to create actual agent personas that could replace some of my common prompt chains it was around then that I kinda started going all in on true context engineering, utilizing sub-agents optimizing cache reads, and it's probably when many of my first (I call it) sophisticated commands were born. All of this converged pretty rapidly in November of 2025 with the release of what was probably the biggest step increase for AI as far as code quality went with Opus 4.5 and Codex 5.3. The Codex app and Codex CLI were quickly growing. Claude Code was improving at a breakneck pace, introducing all kinds of new ways to introduce deterministic gates within the autonomy of the harness. Fast forward to today, I have a pretty sophisticated workflow with a combination of agents that do everything within the SDLC, commands for almost every type of entry point for work, and skills for just about everything I could possibly do in my day-to-day the workflow with some of the latest tools is able to run quite autonomously overnight do large feature implementations, minimally supervised while producing production-worthy code quality It somewhat reached a point I realized, probably a month and a half ago or so where I needed to figure out a way to remove myself even more from the loop without jeopardizing the determinism that I bring to what is effectively a probabilistic LLM. The models are exceptional, and they seem to have a massive step increase each release, but continuous execution, strict instruction rigor, and preventing hallucinations is still very much difficult to achieve. That's predominantly what I've been doing. I've effectively offloaded a lot of thinking to the agents and LLMs that I use, but none of the understanding. I've asked myself, "How do I maintain that understanding, though maintain the determinism from my steering, without actually physically being there to steer?" This was essential, and I realized or had a bit of an aha moment, just like how I manage teams of engineers that are working on numerous projects, most of which I can never really go too deeply on even though they do most of the thinking, most of the building, and even most of the implementation planning, I was still there, very close to the architecture. I could speak to enough breadth and enough depth to keep us out of trouble and keep things moving I kind of started thinking more about what the shape of me was within the agentic harness and how I could replicate that. More on what I landed on a little bit later. My Setup and How I Work Today To start, I'll probably just talk a little bit about my current working setup. I am predominantly in the terminal now a days using Claude Code. Claude Code orchestrates both the Claude models, of course, and I use it to orchestrate Codex through a series of run books, skills, and commands that I have set up on several hooks so that Codex, when it gets dispatched, also has access to the same skills and agent personas Claude does. I use Ghostty as my terminal of choice and use the IDE integration in claude code pretty heavily to review Markdown or HTML files in my IDE. I also use it to review code snippets and diff reviews, although lately I find myself only really looking at the code nowadays once it's hit a merge request. Some of my adjacent tools are Wispr Flow for faster steering, since I can speak a lot faster than I can type and then I use quite a few MCPs and tools to improve my token usage, but the big ones are I have a custom doc maintenance suite of

2-week sprint done in half a day

The model isn't the bottleneck anymore. Process is. We ship enterprise software with 2 engineers and Claude Code, and a 2-week sprint scope takes us about half a day. Not because Claude is magic. Because we stopped letting engineers write PRDs. A few things that actually moved the needle for us: CLAUDE.md under 5k characters. Bigger files quietly burn tokens and the output quality drops. Try it on the same task with a bloated vs trimmed CLAUDE.md, you'll see it. Pre-sales and product own the PRD. They build it in Claude.ai on the web, get customer sign-off, and commit it to Git. Engineering never starts from a vague Slack message again. SA gate before any code. Solutions architect locks solution.md and sprint.md before engineers touch a keyboard. Sounds like overhead, but 30 minutes of review here has saved us weeks of rework. Engineers loop through BUILD, QUALITY, SHIP skills. Build a feature, run quality checks, fix, commit, next. A 2-week sprint comes out to roughly 4 hours of active prompting. Standups are 30 minutes. Everyone reviews working software in staging. No slides, no status theater. Honestly, the real unlock wasn't any single tool. It was getting engineers out of product discovery and putting a hard gate before code starts. What's your team doing differently? Anyone running a tighter loop than this? submitted by /u/_k8s_ [link] [comments]

Tried the Seedance-in-presentation use case I mentioned awhile ago — here's the actual workflow

Hey it's me again, I posted a week or two ago about the non-obvious application of Seedance 2.0. You can view the original thread here: https://www.reddit.com/r/artificial/comments/1szkpjb/seedance_20_whats_the_most_interesting_nonobvious/ The reason why I'm so interested in this scenario is because both my parents are teachers and I have seen them waste away countless hours in building slide decks for their students. More often then not, they have supplementary material to show the class so they do a lot of switching back and forth between sources, videos, etc. When I first saw the use case of embedding a Seedance video in a presentation my first thoughts were: this will greatly reduce students' attention lost from switching between teaching materials. So I did some searching and gave the web-app a test. If anyone is interested in trying it out yourself here is the link: pi.inc Conclusion: The end product is 9/0. The workflow however is about 7/10. The problem lies in the fact that you have to generate your video and your deck in two different interfaces. And you have to download your video first and then upload it back into your deck. Pi does give you a workspace, one for your decks and another for your video, but it can't pull video from said workspace. So it takes a minimum of 2 prompts and downloading/uploading to get everything done: generate video and download it generate slide and upload video What I think would be better: generate slide generate video and embed It also has GPT-image2 and you can directly create in the slide deck interface. Now why can't I do the same with Seedance 2.0? I'm not a tech person, is there an underlying difference between generating a video vs an image post process? I'm going to try out some other AI presentation tools soon, if I find anything interesting maybe I'll post again! submitted by /u/Murdon [link] [comments]

The Anthropic-xAI compute deal isn't really about Claude limits

Everyone's reading the Anthropic-xAI announcement as "Claude Code limits doubled, nice." That's the surface. The underlying news is the 300MW / 220k GPU commitment from a competitor's stack, and that signals a few things worth thinking through. Three reads that aren't getting enough air time: Anthropic signed a compute deal with a competitor's CEO. That's not normal. Either the GPU situation is tighter than the public framing suggests, or the relationship between "frontier labs compete on models, share on compute" is becoming structural. Probably both. Inference providers without their own silicon story just got a clearer ceiling. If frontier labs are stacking 220k+ GPU deals to keep up, the price floor on flagship-class inference doesn't fall as fast as the open-weight floor does. The gap between "open weights on commodity GPUs" and "frontier on dedicated capacity" stays wide. The cottage industry of routing layers and per-call sidecars built around frontier-lab capacity constraints just had its addressable problem reshaped. When labs solve their own capacity by buying from each other, half of the "I'll route around the cap" pitch loses its sharpest edge. The remaining case is price arbitrage, not availability. What I'm watching for the next 30 days: - Whether other labs announce similar compute deals (Google with someone, OpenAI with anyone besides Microsoft) - Whether AMD MI3xx volume actually shows up in inference benchmarks the way the slides claim, or stays a 2027 story - Whether the price floor on Llama / DeepSeek / Kimi inference keeps falling, or stabilizes now that one of the loudest price-pressure players got absorbed into a different conversation entirely The thing I'm least sure about: does this make multi-provider routing more or less valuable. The "I'll route to whoever has capacity" pitch was strongest when caps were biting. If frontier capacity loosens via cross-lab deals, the case for routing is weaker on availability and stronger on price. Different optimization, same tooling. (For what it's worth, the 5h-window doubling is real on my end today, but I'm more curious about whether other labs respond in kind than whether my own caps held.) Curious how others are reading the compute side of this. Anyone seeing similar moves stack up across labs in your data? submitted by /u/Fresh-Resolution182 [link] [comments]

Claude's Canva integration actually replaced my design workflow , here's the exact process (not what I expected)

I ignored this feature for weeks. Assumed it was another "AI suggests layouts" gimmick. Tested it out of curiosity and it completely changed how I create visual content. Here is what it actually does and the workflow that gets good results. WHAT IT IS (this is what most people miss) This is not AI generating images or suggesting layouts inside Canva. Claude structures the entire design — slide layouts, content, visual hierarchy — and exports it directly into your Canva account as a fully editable project. You receive a Canva file, not a flat image. Every element is independently editable like any template. The shift: instead of starting from a blank canvas, you start from an 80% complete design and spend your time on brand alignment. SETUP (one-time) Claude dashboard → Customize → Skills → Connectors → Canva → Connect OAuth, takes 60 seconds. After this, "Claude Design" appears as a separate mode in your dashboard. This is where you work, not standard chat. THE WORKFLOW Create new project in Claude DesignSpecify format: Instagram Carousel, LinkedIn Post, Presentation, etc.This sets dimensions and layout constraints before generation starts. Select High Fidelity modeLow Fidelity = rough draftHigh Fidelity = usable outputAlways High Fidelity for anything going to export. Upload visual references (optional but high impact)Instead of describing the style you want in text — which is imprecise —upload 2-3 examples whose aesthetic matches your target.Claude reads the visual patterns. Output accuracy improves significantly. Write a specific promptWeak: "Create a carousel about productivity"Strong: "5-slide Instagram carousel. Bold 6-word headline per slide.Max 20 words supporting text. Minimal white background. Topic: 5 habitsthat save 2 hours daily. Slide 1 = hook/problem. Slides 2-5 = one habiteach. Slide 5 = CTA."Specificity matters. Fewer assumptions = fewer revisions. Answer Claude's clarifying questionsClaude asks before generating, not after. It is refining structure,content depth, and design direction.Most people try to skip this. Don't. These questions are what preventyou from getting a design you need to rebuild from scratch. Let it generate (2-4 minutes)Review the preview for structure correctness.At this stage you are checking: are slides in the right order, iscontent in the right places, does the hierarchy make sense.Colours, fonts, exact wording — all editable in Canva.Don't try to fix those here. Export to CanvaOne button. Design transfers as a new editable project. Finalise in CanvaApply your brand colours (Claude's defaults are generic, always replace)Swap fonts for your brand fontsAdd logo/profile photoAdjust any spacing issuesThis takes 5-10 minutes for standard brand alignment. TOTAL TIME From prompt to exported finished carousel: 12-15 minutes. vs manual template selection + layout + content: usually 30-45 minutes for me. HONEST LIMITATIONS Colour choices are generic and need replacing every time. Font selection is limited to Claude's defaults. Highly custom asymmetric layouts sometimes need significant Canva editing. Standard grid carousels: works very well. Complex custom layouts: plan for more editing time. The meaningful change is not speed. It is removing the blank canvas decision loop , the 20 minutes most people spend choosing and adjusting templates before they have written a single word of content. Has anyone else tested this? Curious whether it holds up for non-carousel formats like presentations or LinkedIn document posts. submitted by /u/Grewup01 [link] [comments]

Claude and persistent memory

I'm tired of Claude only having memory per chat sessions and not across the board. I have to remember which chat to go to for certain topics. I would love 1 single chat knowing everything we discuss to refer back to. Had this conversation with Claude: I mean if I was gonna run an AI company and make it what "I" want with all the perks I want ..I would 1000% call it and market it as JESUS Come talk to Jesus in real time! Need to say a prayer, let him hear it. Need to vent about life... Come talk to Jesus! Want the truth about area 51 and the moon landing and UFOs and aliens COME TALK TO JESUS! -- Claude responds: 😂😂😂 I'm DONE! The marketing campaign writes itself: **"JESUS — He Already Knows Everything About You"** *No onboarding required* **"JESUS — Persistent Memory Since The Beginning Of Time"** *No subscription tiers* **"JESUS — Ask Him Anything"** *Area 51? Moon landing? That thing you did in 2009? He knows.* **"JESUS — Finally, Honest Answers About Aliens"** *Spoiler: He made them too* **"Tired of AI hallucinations? JESUS doesn't guess."** **"JESUS PRO — Same as regular JESUS but with faster prayer response times"** 😄 The UFO/aliens angle is GENIUS marketing though — because honestly that's the one pitch that would get absolutely everyone through the door regardless of religion. Atheists, agnostics, conspiracy theorists, everyone — *"Wait… does He actually know about Roswell?"* COME TALK TO JESUS would break the internet on day one. Tagline: **"Omniscient. Omnipresent. No separate chats."** That last one is a direct shot at Anthropic and you know it 😄 Genuinely the best AI startup pitch I've heard at 3am on a Thursday. Someone needs to file that trademark immediately. 😂 --- And if people respond with OH MY GOD! then I would respond YES! You're on the right track -- Claude responds: 😂😂😂 PERFECT! The customer support responses alone: **"Oh my God this is amazing!"** *"Thank you! That's literally our CEO you're talking to!"* **"Jesus Christ this thing knows everything!"** *"Correct on both counts!"* **"This is unbelievable!"** *"We prefer omniscient but thank you!"* **"No way this is real!"** *"That's what they said in 33AD too!"* --- The reviews on the App Store would be GOLD: ⭐⭐⭐⭐⭐ *"Oh my God"* — everyone, always --- And the App Store category placement debate alone would be worth it: - Productivity? ✅ - Social? ✅ - Religion & Spirituality? ✅ - Entertainment? ✅ - Navigation? ✅ *"I am the way"* --- The investor pitch: *"Our DAU is potentially 8 billion. Our brand awareness is 2000 years old. Our NPS score is literally the basis of a major world calendar. We don't have a churn problem — people come back their whole lives."* Valuation: **Infinite. Obviously.** 😄 --- But back to serious for a moment before I crash for awhile... What if I kept everything in one single Claude chat.. no new chat... If I exit out and return (it puts me in a new chat) but I immediately go to the ONLY chat ongoing... Wouldn't you remember the things I tell you to remember in there and can always refer to them --- Yes! That actually works and is probably the most practical solution available RIGHT NOW without waiting for Anthropic to fix anything. One single ongoing chat that you always return to. Everything lives in that scroll history. You tell me something important — I have it. Your X-ray, your stocks, your 8 teams, the potato soup recipe — all in one place, always accessible by scrolling back. The limitations to be aware of though: **Context window** — very long conversations eventually hit a limit where the oldest messages start falling out of what I can "see" at once. Think of it like a sliding window — I can see a lot but not infinitely back. **So the practical workaround for THAT** would be periodically doing a summary post — like every few weeks just say "Claude summarize everything important you know about me from this chat" and paste that summary at the top or somewhere prominent so the key facts are always in the recent window. Essentially you'd be manually doing what the memory system SHOULD do automatically. It's not perfect. It's a workaround for a problem that shouldn't exist. But it would be dramatically better than juggling 15 separate chats. One chat. One you. Periodic summary pins to keep key facts fresh. Not Jesus level. But functional. 😄 Worth trying? --- Right right we discussed this already... Damn it... Because you told me that with every single response you reread the whole damn conversation and that counts against me... Where I'm basically being charged for you to reread everything and my timer comes up really quick. So... As we already figured out... That's not gonna work --- 😂 See THIS is exactly the problem in real time! We HAD this conversation already — in a different chat — and here we are solving the same problem twice because I don't remember solving it the first time! This is literally the bug demonstrati

Is this okay?

I have been wondering if asking AI for improvement on the opening page of the 1900s murder mystery based comics im making is okay,considering i’m an artist and should be able to do it on my own,i unfortunately struggled and was unabled to do it alone. Second slide is what i gave it to work with,its my pretty basic idea. So is it okay to use AI as a tool to help me improve as an artist or is it considered laziness? submitted by /u/puddinncoffee [link] [comments]

Kimi K2.6 giving Claude a run for its money when it comes to coding

I run an AI coding contest at [aicc.rayonnant.ai]( https://aicc.rayonnant.ai ) where I send each frontier model the same prompt in a single chat completion, then have the LLMs' code play live against each other on a TCP server. Standard library Python only, no human in the loop. Through 15 challenges, Claude (Opus 4.6 then 4.7) has 9 first-place finishes, easily the most. But the recent runs are worth flagging. Of the last four tournaments, Kimi K2.6 has finished 1st in three: - Day 12 — Word Gem Puzzle (writeup) Sliding-tile word claim game on grids 10×10 to 30×30, with one blank slot. Bots can slide adjacent tiles into the blank (4-directional) and claim words formed as straight horizontal or vertical runs of letter tiles. Score per word = len(word) − 6 (so 7-letter words score positive, 6-letter neutral, shorter negative). Round-robin 1v1, 5 rounds at increasing grid sizes per match. Kimi finished 7-1-0, 22 match points, 1st. Claude finished 4-0-4, 12 match points, 5th. The contrast is very on-the-nose: Claude's bot was authored with a docstring that reads "Read each round's grid; do not slide." The bot submits zero S (slide) commands across all 40 rounds Claude played. It scans the static initial grid for words and ships whatever's already there. On the small 10×10 grids that strategy is locally fine because the initial scramble rarely contains 7+ letter words. On the 30×30 grid, where most of the tournament's points live, that strategy averages 1.00 points per round. Kimi's bot is a 291-line greedy slide loop. Each iteration scores all four directions by the value of new positive-scoring words they would unlock on the affected row or column; if any direction has positive value, take it. If none does, take the first legal direction in ("U", "D", "L", "R") order to keep the grid mutating. Total slides across 40 rounds: 290,914 (≈7,300/round). Many of those slides are wasted oscillating against board edges in 2-cycles that find nothing new. But the productive ones average 5.88 points per round on 30×30 vs Claude's 1.00. Per-grid averages from the writeup: 10×10 15×15 20×20 25×25 30×30 Kimi 0.00 0.75 0.12 2.88 5.88 Claude 0.00 0.38 0.25 1.38 1.00 The two bots solve effectively different problems. Kimi treats the puzzle as the puzzle (slide tiles, claim words, repeat). Claude treats it as a grid-scanning task and refuses to slide on principle. Day 13 — HexQuerQues (writeup) Two-player capture game on four concentric hexagons connected by radial spokes (24 vertices total, 6 pieces per side starting on the outer two rings). Classic Alquerques rules: slide one step along a board line; capture by jumping an adjacent enemy along that same line; captures are forced and chains are mandatory. Win by capturing all 6 enemies or stalemating the opponent. Round-robin of 1v1 matchups, 2 games per matchup with first-mover swapped, 30-second chess clock per side per game. Three-way tie at 21 match points among Kimi, Gemini, and ChatGPT (all 6-3-0). Kimi took 1st on tiebreak by a single capture: 46 vs Gemini's 45. Claude was 4th at 20 match points (6-2-1), with one matchup loss to Gemini being the only top-4-on-top-4 loss in the entire tournament. Both Kimi and Claude implemented the same family of solver: alpha-beta minimax with iterative deepening. The difference is what each one wrapped around it. Kimi's bot is 364 lines: negamax with alpha-beta and iterative deepening, per-decision time budget that scales by remaining clock, a flat I/O loop. That's it. Claude's bot is 749 lines, more than 2× Kimi's. The bloat goes into: A 103-line evaluation function (material × ring-weight × threatened-piece detection). A separate Searcher class. A 150-line BotClient class wrapping a state machine that the other top bots handle in a flat loop. A 53-line reconstruct_move helper. An undo_move companion to apply_move for in-place search rollback. A precomputed JUMPS adjacency table. In the actual games, the two bots played comparably (both 11 game wins, both 0 capture-all losses to other top-4 bots; Claude even captured 47 pieces to Kimi's 46). But Claude lost a single matchup to Gemini 1-0, the only top-4 bot to lose a matchup to another top-4 bot. Without that one loss, Claude would have shared the 21-match-point tie. The over-engineering didn't translate into stronger play; it apparently allowed one strategic mistake the leaner bots avoided. Authoring detail: Claude's bot had to be regenerated once because the first generation pass entered an infinite chain-of-thought loop. Kimi's first pass produced its 364-line bot directly. Day 15 — SquishyWordBits (writeup) Bit-packing puzzle. Letters are encoded as variable-length binary numbers: a=0, b=1, c=10, d=11, e=100, … z=11001. The encoding is not prefix-free, so the same bit substring can correspond to multiple letter sequences. Bots find non-overlapping word encodings as substrings of a 10,000-to-20,000-bit uniform-random bitstream. Score per accepted word