Falkon Snow

@TheReal_NEWO @DIT_eo @VNGemeenten Ok, dankjewel voor de bevestiging dat je niet verder komt dan daadwerkelijke inhoud. Maar ik snap dat als mensen je aan de schaft naar boven likken op niks anders dan populisme, je niks anders meer interesseert.

Joined a new AI-native company this week and it’s kind of wild how different it feels already. The laptop arrived, I logged in, and an agent basically took over from there. It set up my dev env, pulled repos, fixed dependency issues, got permissions approved, pointed me at the backlog, linked the architecture docs, and surfaced the Slack debates I actually needed to read before touching production. When I needed context on something, I asked the agent and it found the exact thread from months ago explaining why a decision was made, who owned it, the related Linear issues, and the PRs connected to it. I’ve only been here 3 days but it honestly feels like I’ve worked here for a year because the usual friction and scavenger hunt for context just isn’t there anymore. We should probably stop calling this “onboarding” and rename it to “mounting” because this feels a lot more like mounting a distributed filesystem called “institutional memory” than slowly getting drip-fed context over 6 months.

@yazins @OpenOats @ibmokdad Thats awesome. @ibmokdad any chance you could explain me how to use hermes to make a similar windows version based on latest version of OpenOats?

@falkonsnow @OpenOats haha.. wait i spotted a windows fork here by @ibmokdad github.com/ibrahimokdadov…

OpenOats v1.40 → v1.65 — 25 releases; 2,323 ⭐ 📅 Calendar auto-titles sessions 👥 Meeting families: shared history, prep notes, default folders 📷 Camera-based meeting detection (no more false positives) ✍️ Live scratchpad feeds the AI 🔊 Replay raw mic + system audio

Když Čína omezuje VPN, je to jasný důkaz totality. Když Rusko omezuje VPN, je to jasný důkaz totality. Když EU chce omezit VPN, je to jasný důkaz.... svobody a demokracie?

You can now give Hermes, Claude Code, and Codex infinite memory. For free. Agentmemory is trending on GitHub with 4,000+ Stars. It records what Claude does during your coding sessions. Compresses it with AI. Injects relevant context back into future sessions. CLAUDE md dumps 22,000+ tokens into context at 240 observations agentmemory: 1,900 tokens. same observations. 92% less. at 1,000 observations, 80% of your built-in memories become invisible. agentmemory keeps 100% searchable. benchmarked on 240 real coding sessions, not projected The numbers are wild: → Up to 95% fewer tokens per session → 200x more tool calls before hitting context limits → 100% open source 1000 GitHub stars already on one week. I've shipped 50+ production agents. Context limits have killed more sessions than I can count. This changes how you build with Claude Code. No more re-explaining your codebase every session. No more losing decisions after /compact. No more starting from scratch. Claude finally remembers. github.com/rohitg00/agent… ♻️ Repost if you're tired of context limits. 🙏 Follow for more production AI tools.

This works really well btw, at the end of your query ask your LLM to "structure your response as HTML", then view the generated file in your browser. I've also had some success asking the LLM to present its output as slideshows, etc. More generally, imo audio is the human-preferred input to AIs but vision (images/animations/video) is the preferred output from them. Around a ~third of our brains are a massively parallel processor dedicated to vision, it is the 10-lane superhighway of information into brain. As AI improves, I think we'll see a progression that takes advantage: 1) raw text (hard/effortful to read) 2) markdown (bold, italic, headings, tables, a bit easier on the eyes) <-- current default 3) HTML (still procedural with underlying code, but a lot more flexibility on the graphics, layout, even interactivity) <-- early but forming new good default ...4,5,6,... n) interactive neural videos/simulations Imo the extrapolation (though the technology doesn't exist just yet) ends in some kind of interactive videos generated directly by a diffusion neural net. Many open questions as to how exact/procedural "Software 1.0" artifacts (e.g. interactive simulations) may be woven together with neural artifacts (diffusion grids), but generally something in the direction of the recently viral x.com/zan2434/status… There are also improvements necessary and pending at the input. Audio nor text nor video alone are not enough, e.g. I feel a need to point/gesture to things on the screen, similar to all the things you would do with a person physically next to you and your computer screen. TLDR The input/output mind meld between humans and AIs is ongoing and there is a lot of work to do and significant progress to be made, way before jumping all the way into neuralink-esque BCIs and all that. For what's worth exploring at the current stage, hot tip try ask for HTML.

@jayfarei @raycast Beautiful. Thanks

@falkonsnow I used theme "forest" from code images by @raycast => #lineNumbers=false&theme=forest" target="_blank" rel="nofollow noopener">ray.so/#lineNumbers=f…

This is quite beautiful, 43 lines and you have your own code agent in a sandbox at POST /agents/<agent-name>/<id> 🤩

@jayfarei Bump for visibility. My eyes and brain need this color scheme. @jayfarei

@jayfarei @jayfarei can I ask what color scheme you use here for the code markup? My eyes love it.

@shannholmberg Bookmarkable

How to build a brand from scratch using Paper, Codex + GPT image model 2 Since two weeks back, I´ve done most of my design work in paper And I´m genuinely impressed by what you can do with their MCP Combined with claude & codex you can pretty much build out a brand in an hour or two 1. Brainstorm a name for you product or service 2. find a good design system you can iterate on or come up with one from scratch 3. create a moodboard of things you like and want the brand to be about (images, colors, shapes, elements, you name it) inside paper 4. select the moodboard and prompt codex to create a brand brief (hooked up to paper mcp) to create the inital brand book in code 5. Use the new brand book you created and copy as PNG, take that image into ChatGPT and use GPT model 2 (with the brand prompt) to create the brand book 6. You will get a finished and well-done 10 paged brand book Now you can use that to create anything in line with the brand: Landing pags, lead magnets, ads, anything you can think of Comment "bookmarkable" + RT and I´ll send you the brand prompt and step by step guide in DMs.

I automated my content engine and 2 hrs/day dropped to 10 min [ what’s new in v2 ]: - 9 platforms scraped while I sleep → 2,000+ topics/day - a 5-signal scoring brain that filters down to the 10 that matter - voice DNA writer.. same tone, different structure every time - a self-learning loop that remembers every approve and decline - profile DNA — knows exactly what goes viral on MY account v1 was a brain with no body v2 has eyes, a filter, and memory + fully automated Here’s how to build it step-by-step ↓ [ The architecture]: /content-engine ├── scrapers/ (9 platform scrapers) ├── extension/ (chrome ext for X, linkedin, reddit) ├── ai/ │ ├── ranker.py (5-signal scoring brain) │ ├── content_writer.py (voice DNA + structures) │ ├── profile_analyzer.py (your positioning DNA) │ └── sentiment_analyzer.py ├── publisher/ (export + time slot scheduling) ├── gui/dashboard.py (streamlit command center) ├── ingest_server.py (local server on localhost) └── data/content_engine.db (everything stored locally) let me walk you through each layer ↓ LAYER 1: Research engine 9 sources scanned 24/7 (X, reddit, YT, HN, github, trends + chrome ext for reddit and linkedin) every post you scroll past gets tagged and stored locally LAYER 2: Scoring brain every topic scored on 5 signals: - freshness (0.20) - velocity (0.25) - virality (0.25) - relevance (0.20) - uniqueness (0.10) velocity 8+ → forced min score of 7. catches late bloomers that suddenly explode 2,000 topics → top 10 ranked LAYER 3: Voice DNA writer not one structure every time. system picks the format: - short take - tactical playbook - QT contrast - contrarian - resource drop - proof post a voice guardian auto-rewrites anything that fails: lowercase ratio, no hashtags, no corporate words LAYER 4: Dashboard Streamlit dark theme. 5 tabs review queue = tinder for content. swipe approve, swipe decline LAYER 5: Publishing no auto-posting. zero account risk approve → pick a slot (8am / 12pm / 5pm) → exports a .txt → copy / paste / post also auto-drafts a linkedin version of every approved tweet LAYER 6: Self-learning loop every click logged. weekly the system embeds your decline notes and re-tunes the scoring brain month 1: you approve 30% month 3: 70% pre-filtered month 6: 10 min/day LAYER 7: Profile DNA analyzes your past tweets. tells you exactly which pillars, formats, and hooks perform best on YOUR account the scoring brain uses it to prioritize what already works for you daily run: open dashboard → 10 min reviewing → post 3x → close total cost: ~$15/month everything else: local, sqlite, no cloud, no subscription unfortunately I couldn’t paste in long-form format initial description which was made before but if this hits 2,000 likes I drop the full build guide with every prompt you need to ship it in claude code reply "ENGINE" + RT and I'll DM you access to test it (follow me first so I can write) save this so you don't lose it

@DeRonin_ ENGINE

@socoloffalex @krealdesign Bummed out you deleted the post. Why?

@krealdesign Not really, I mean not every client needs $50,000. With an amazing branding, good workflow and installed Claude skills it’s actually does the job

@socoloffalex Love to see the turnaround Alex. Conscious use of AI combined with solid expertise and creativity accelerate your speed and scope of impact!

@doser_i85668 What comedian is this?

What Are You Studying??? 🤔🤔

After reading @AnthropicAI blog on Agentic AI. spent some time to create a mental model to understand how to design, and explain Agentic AI architecture Define a task/goal - what you want agent to do achieve? 1. Orchestration layer : it is your control panel 3. Agents layer: this layers made of agents (multi /specialised) 4. tools: your tools are made of this layer (web search, DB, APIs etc) 5. memory: this is the brain to store information - long or short term etc. 6. monitoring : This is the most crucial to monitor each and every step 7. Reliability & failure management: identify errors, retry, fallback, involve human 8. Governance and security: compliance, audit, auth etc.

This is what Palantir is.