PABLO

This is huuuuge! Now Hermes can search X using my Premium sub rather than burning paid X API calls! Make sure you’re on v0.14.0 then run: hermes auth add xai-oauth Then run hermes tools and ensure X search is enabled Boom!

It’s here: Try VoiceClaw Realtime on iOS and watchOS now! Download TestFlight for free on iOS, then use the link in the replies to download the latest beta via TestFlight, as well as the macOS Companion app. To get started after downloading TestFlight and installing the app: Add an optional OpenAI API key in the macOS Companion app or directly in the iOS app. Even better, if you have OpenClaw installed, no API key is needed; you can connect VoiceClaw Realtime to OpenClaw’s OpenAI connection via the macOS Companion app simply by entering the path to your “~/.openclaw” directory. It’ll generate a connection token that your phone can use to reach OpenAI models and automatically use your OpenAI ChatGPT Plus/Pro subscription via OAuth. Sync your settings from the macOS Companion app to the iOS app via the iOS in-app QR code reader. If you also want to try the Apple Watch app, which installs automatically or via the iOS Watch app, make sure to sync your settings to the watch app via the in-app “Sync to iPhone” button. Enjoy real-time conversations with GPT-Realtime-2, GPT-5.5-Instant, and OpenClaw on both iOS and watchOS now. VoiceClaw Realtime is likely coming to the App Store soon, so download the beta now while it’s free. On iOS, the OpenClaw modes (“iPhone”) route your requests from GPT-Realtime-2 to OpenClaw if you have Tailscale installed on your phone and macOS device, enabling full OpenClaw capabilities. GPT-Realtime-2 has been enhanced with lots of direct, on-device iOS features, so ask it what it can do on your phone, even without contacting OpenClaw. Also try GPT-5.5-Instant mode by going to the iOS app settings and choosing “Instant” in the route options. It gives you lightning-fast, in-depth responses from a hybrid GPT-Realtime-2 conversation partner that uses GPT-5.5-Instant, for when you just need good answers (including from Instant's web-search capabilities) but don’t want to connect to your OpenClaw agent. Please provide feedback here on X, via the iOS TestFlight app, or by email. See the link in my bio. Feedback is strongly encouraged. And please comment in the replies to let me know you’re trying it out! P.S.: Version 1.4.45 is available now, and an even better beta, version 1.4.58 / 1.4.59, will be available via TestFlight at the same link by 3:00 PM ET.

You can literally create anything with AI right now. I made a full eCommerce product commercial with @EaseMate_AI_ GPT Image 2 → storyboard (cast + scenes) Seedance 2.0 → full cinematic video Save this — full prompt below 👇

Zombie Scavenger hit 13.2M views this week. One scene stayed with me — a robot walks into an abandoned bar. An ostrich is hiding behind the counter. I tried to recreate that 15 seconds. A small tribute to MX-Shell Created with Seedance 2.0 on @higgsfield_ai Want the exact prompt? Keywords in the comments.

LTX2.3 Pixar-Style CGI Toon LoRA Get that high-end animated film look- smooth surfaces, expressive eyes, and cinematic lighting. Perfect for character-driven storytelling. huggingface.co/Andro0s/LTX_2.…

Turn any website into an API with /browser-to-api. This skill analyzes network activity, CDP logs, and website behavior to generate a custom OpenAPI spec. Watch Codex one-shot a fully documented OpenTable API client from a single prompt 👀

Hermes Atlas is a curated collection of Hermes resources: tools, plugins, skills, workspaces, integrations, and more. Check it out👇

so we built psql_bm25s. exact BM25 retrieval. native Postgres access method. ~23x faster than pg_search on the standard benchmark. retrieval stops being a budget item. the harness stops rationing. the agent gets to look things up like it should have the whole time.

This is a gamechanger. The new LipDub IC-LoRA for LTX 2.3 is the best AI lip sync tool I've ever seen. Credit u/dobutsu3d

@jumperz @Spark_coded So do you use it beside your hermes?

you probably heard of @Spark_coded but you still don't get it.. I'll walk you through it all by explaining it super simply.. 1. spark improves while you're away it's a local agent that lives on your machine, talks to you in telegram, and keeps training itself in the background >you close your machine.. spark keeps working >wake up, it's sharper than last night.. that simple 2. how it works in one picture you → telegram → spark's brain → your llm of choice → answer back to you 3. spark has a brain called the intelligence builder, think of it as a dispatcher, so every message you send, it decides: >is this chat or a build task >which model handles it (claude, codex, glm, local, whatever you brought) >what memory to pull >which specialist chip to wake up you just type one line and it routes.. 4. now the interesting part is that spark has "chips" >a chip is a specialist, one for trading, one for content, one for code... one for startups etc .. so each chip has a score: >starts weak then it gets graded every time it runs.. kinda think of it like pokémon but for skills 5. the loop: baseline 42 → trial 68 → review → promote 81 spark runs the chip overnight, tries new rubrics, new tools, new approaches then scores each attempt. if the new version beats yesterday → promote. if not → retire 6. the wedge every other agent in 2026 promotes itself because it *sounds* better when spark only promotes itself because it *scored* better.. benchmark, rubric, replay, or human review, basically one of those four has to gate the change.. otherwise, it doesn't get to write back. 7. what spark remembers: not every random chat, but lessons, boundaries, playbooks, stuff worth keeping. you tell it your taste once, it carries that forever on your local machine and it never leaves unless you opt in. 8. what gets shared with the swarm: nothing private like ever, not your secrets..not your raw work or your memory and the only proven pattern is the chip version that scored higher.. 9. how it actually feels: day 1: spark is generic... answers are mid. day 30: it knows your voice, your stack, your habits, your no-go zones.. day 100: it has chips you didn't even ask for, nightly loops improved them while you weren't even looking and your agent just compounds. 10. tldr spark is more than an agent..it's a recursive agent stack: >chat door (telegram) , hoping for discord later >runtime (the brain) >chips (specialists) >scored loop (the engine) >local memory (yours) >optional swarm (only proofs travel) 11. the bet is simple personalization > model this about what would happen in 12 months, everyone's spark will look different because everyone's chips will have trained on different work. well, sure I’m not replacing Hermes with Spark.. Hermes is still the supervisor of my stack but Spark sits above the mess as the pattern layer..It watches the runs, scores what worked, catches repeated workflows, and tells me what should become a reusable chip/skill.. worth giving it a try if you’re already running agents, the fun part is that later you can train any chip around the work you actually repeat. your content chip, research chip, trading chip, coding chip, startup chip, whatever... and that’s where it gets interesting, because the agent doesn’t just remember your preferences and it starts building specialists around your own workflow.. this is the surface tho.. there's still canvas that shows what spark saw and learned, the autoloops and alot more.. but this is enough to understand what it is about... keep an eye on @Spark_coded and @meta_alchemist , a lot is happening in the next few days..

Crochet ads are blowing up on Meta right now. Not because they're cute. Because they stop the scroll. Here's the 4-step KNIT Method to make them with AI in under an hour 🧵

Created this race using GPT Image 2 and Seedance 2.0 on @TapNow_AI Prompt Follow the storyboard strictly in exact order from Panel 1 to Panel 9. Do not skip, merge, or rearrange scenes. Keep the SAME female cyclist identity across the entire film. No face changes, no hairstyle changes, no helmet changes, no body proportion inconsistencies. Baby pink must remain the dominant apparel color throughout all cycling scenes. Avoid black wardrobe replacements. Preserve realistic nighttime lighting continuity between shots. Maintain the same cool blue tones and subtle red light reflections. Heavy rain intensity must stay visually consistent across all scenes. Water physics must look physically accurate: droplets, splashes, mist, wheel spray, and runoff should behave naturally. Avoid artificial AI motion. Camera movement should feel like real cinema rigs, FPV drones, mounted bike cameras, or stabilized tracking systems. Drone shots must maintain locked framing and smooth movement without random drifting or orbiting. Use subtle cinematic motion only — no excessive shaking or jitter. Keep realistic breathing, body fatigue, pedaling mechanics, and fabric reactions to wind and rain. Preserve shallow depth of field in macro shots and atmospheric haze in wide shots. Keep the environment dark, moody, and cinematic with strong contrast between wet reflections and darkness. Ensure all reflections on asphalt, water droplets, and bike components react naturally to changing light sources. Maintain premium commercial pacing: slow controlled preparation and macro shots transitioning into aggressive high-speed riding sequences. Final output should resemble a high-budget Nike / Rapha night cycling commercial shot during a real mountain storm. Ultra-realistic cinematic night cycling commercial about female endurance cyclists riding through an intense rainstorm in the mountains at night. Premium Nike / Rapha aesthetic with baby pink performance cycling apparel as the dominant accent color. Hyper-realistic documentary look, no stylization, no anime look, no beauty filters. Natural skin texture, realistic rain interaction, physically accurate water behavior, cinematic low-key lighting, cool blue night tones mixed with subtle red rear-light reflections. Heavy rain, fog, wet asphalt reflections, cinematic motion blur, high dynamic range, shallow depth of field, premium sports commercial quality. The film follows a strict 9-panel storyboard structure with seamless cinematic transitions and continuity preserved across every scene. The SAME female cyclist identity must remain consistent throughout the entire video: same face, helmet, glasses, body proportions, baby pink apparel, lighting style, and overall appearance. Maintain continuity of rain intensity, wetness, fog density, and environmental lighting between all shots. Panel 1: Extreme macro close-up of the female cyclist’s eyes and face in heavy rain at night. Focus on soaked eyelashes, wet skin texture, raindrops streaming across the face, baby pink helmet and baby pink face mask visible. Red rear bike light flickers dynamically across her eyes and skin while cool blue night tones dominate the scene. High contrast cinematic lighting, shallow depth of field, subtle breathing motion, intense determined expression. Panel 2: Cinematic medium close-up frontal shot of the cyclist riding aggressively through heavy rain at night. She pedals hard with strong effort and forward-leaning posture. Baby pink waterproof cycling jacket soaked with rainwater. Front bike light cuts through fog and rain with subtle flickering illumination. Wet asphalt reflects red and white lights. Smooth cinematic tracking shot with controlled stable motion and slight natural float. Panel 3: Ultra-realistic macro shot of large raindrops impacting wet asphalt at night. Crown-shaped splashes and overlapping ripples in slow motion. Rough wet asphalt texture, cool blue cinematic tones, subtle reflections from bike lights.

I'm moving and found a fun little use for Claude Cowork: Hand it a floorplan and ask it create a 3D planner. The attached demo (not my real place) was a one-shot. I also had it find all the email receipts for furniture I got over the last few years and put in matching models. Yesterday, I asked it to add a game mode so I can walk through different versions of my new place. The future is SO FUN

the three-tier memory of Hermes agent. AI agents forgets everything when your session ends. Hermes doesn't. it has three memory layers, each at a different speed. 𝘁𝗶𝗲𝗿 𝟭: 𝘁𝘄𝗼 𝘁𝗶𝗻𝘆 𝗺𝗮𝗿𝗸𝗱𝗼𝘄𝗻 𝗳𝗶𝗹𝗲𝘀 MEMORY.md (2,200 chars) and USER.md (1,375 chars). injected into the system prompt at session start as a frozen snapshot. MEMORY.md holds project conventions, tool quirks, lessons learned. USER.md holds your profile: name, communication style, skill level. these files are tiny on purpose. when MEMORY.md hits ~80% capacity, the agent consolidates: merges related entries, drops redundancy, keeps only the densest facts. natural selection pressure applied to memory. the files stay small, but what's inside gets sharper over time. 𝘁𝗶𝗲𝗿 𝟮: 𝗳𝘂𝗹𝗹-𝘁𝗲𝘅𝘁 𝘀𝗲𝘀𝘀𝗶𝗼𝗻 𝘀𝗲𝗮𝗿𝗰𝗵 (𝘀𝗾𝗹𝗶𝘁𝗲 + 𝗳𝘁𝘀𝟱) every conversation gets stored in SQLite with FTS5 indexing. the agent can search weeks of past sessions on demand. when the agent calls session_search: FTS5 ranks matches in ~10ms over 10,000+ docs, an LLM summarizes the top hits, and a concise result returns to context. tier 1 is always present but tiny. tier 2 has unlimited capacity but requires an active search. critical facts live in memory, everything else is searchable. 𝘁𝗶𝗲𝗿 𝟯: 𝗲𝘅𝘁𝗲𝗿𝗻𝗮𝗹 𝗺𝗲𝗺𝗼𝗿𝘆 𝗽𝗿𝗼𝘃𝗶𝗱𝗲𝗿𝘀 8 pluggable providers that run alongside tiers 1 and 2, never replacing them. three worth knowing: Honcho (dialectic user modeling, 12 identity layers), Holographic (local-first, HRR vectors, no external calls), and Supermemory (context fencing that prevents the same fact from being re-stored infinitely). when active, hermes auto-syncs every turn: prefetch before, sync after, extract at session end. 𝗵𝗼𝘄 𝘁𝗵𝗲𝘆 𝗰𝗼𝗺𝗽𝗼𝘀𝗲 𝗶𝗻 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝘁𝘂𝗿𝗻 this is the part most people miss. the tiers compose on every turn through a five-step cycle: 1. turn opens. tier 1 is already in prompt, tier 3 prefetches and prepends. 2. agent responds using all three tiers as context. 3. periodic nudge fires (~every 300s). the agent reflects: "has anything worth persisting happened?" if yes, it writes. if no, it returns silently. 4. memory written to MEMORY.md on disk. invisible this session because the prefix cache stays warm. 5. session closes. tier 2 logs the transcript, tier 3 extracts semantics. next session opens with the new state. agent memory today is either always-on but shallow (stuff everything in the prompt) or deep but passive (vector store that never fires at the right time). hermes composes across both: tiny always-present files for critical facts, full-text search for deep recall, external providers for semantic modeling, all orchestrated by a nudge that decides autonomously what's worth saving. the agent doesn't just store memories. it curates them under pressure. i wrote a full deep dive (article below) covering hermes agent's memory system, self-evolving skills, GEPA optimization, and how to set up multiple specialized agents on your machine.

Finally you can get the storyboard method The actual master prompt behind all those viral posts For free, without paywalls, without courses and without BullSh*t See full breakdown below 👇

🚨 Pixal3D is now live on fal! 🧊 High-fidelity 3D assets from a single image 🎯 Pixel-aligned back-projection for direct pixel-to-3D correspondence 🎨 Detailed geometry with PBR textures, ready for downstream pipelines

Save your credits and use open source models when you can. The new LTX 2.3 lipdub LoRA paired with Chatterbox TTS voice cloning model is the best workflow for lip syncing, change my mind.

crazy.. AI made 3D Pixar animation with just one prompt