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Parker Fintech Startup Files for Bankruptcy
Parker, a well-funded startup offering corporate credit cards and banking services, has filed for bankruptcy and is widely reported to have shut down.
Searchable WAR.GOV/UFO Files: 55,256 Slides Now Online
Title: Explore the WAR.GOV/UFO Files: 55,256 Slides Now Accessible Online Introduction The War.GOV/UFO Files, a comprehensive archive containing 55,256 slides, …
Tilde.run: AI Agent Sandbox with Versioned Filesystem
Tilde.run: Revolutionizing AI Development with a Versioned Filesystem Tilde.run is a cutting edge platform designed to streamline AI development by providing a …
Raptor: Fast, Energy-Efficient S3 Uploads for Small Files
Rapid and Energy Efficient S3 Uploads for Small Files with Raptor In the evolving landscape of cloud storage, optimizing small file uploads to Amazon S3 is pivo…
AI Tool: Generate Files in Gemini Chat for Production
Generate production-ready files directly in your chat
AutoIdeator: Free Open Source Agent Orchestration for Development
[https://github.com/akumaburn/AutoIdeator](https://github.com/akumaburn/AutoIdeator) https://preview.redd.it/rfbgg6e34dyg1.png?width=3809&format=png&auto=webp&s=e436362c48482d09025a394a5e609f67190e6dfa AutoIdeator is an autonomous development system that: 1. Takes a **final goal** — a detailed, multi-sentence description of the intended end result. Describe what the finished project should look like, do, and feel like for the user. **Do not** prescribe implementation steps, phases, milestones, technologies, or task lists — the agents handle planning. The more clearly the desired end state is described, the better convergence will be. 2. Generates improvement ideas via a rotating ensemble of specialized idea agents 3. **Scores and filters ideas** for goal alignment and quality 4. **Critiques ideas constructively** with suggested mitigations 5. **Evaluates strategic alignment** and long-term planning 6. Makes implementation decisions balancing creativity and criticism 7. Implements the plan with parallel coders 8. Reviews, fixes, and commits changes 9. **Runs QA** (build + test verification) 10. **Optimizes slow tests** to keep the suite fast 11. **Verifies goal completion** with 3-step feature inventory, per-feature checks, and auto-remediation 12. **Refactors oversized files** into smaller modules (every other cycle) 13. **Cleans up** temp files and build artifacts 14. Updates project documentation 15. **Records outcomes for learning and deduplication** 16. **Periodically synthesizes synergies** across recent work 17. **Checkpoints state** for pause/resume across restarts 18. Repeats the cycle infinitely until stopped Users can inject suggestions at any time via the Overseer agent, which takes priority over the autonomous idea generation pipeline. Note this system has been tested for some time but only in the dashboard with OpenCode/Claude Code configuration (OpenRouter mode is untested, but I welcome contributions if someone wants to use that mode and notices something is broken).
Mastering AEO: How to Get Cited by AI and Boost Your Visibility
SEO or AEO? Why you’re not showing up in AI answers (yet) This is a consolidation of findings from Neil Patel and Hubspot plus what we have found to work well on our own website. Most business owners are still playing the old game. Some aren’t playing at all. They’re thinking in rankings, keywords, and “getting to page one.” Meanwhile, the ground is shifting under them. Google Search is still dominant, but even it has changed. It’s no longer just a list of blue links. It’s summarizing, interpreting, and answering. And tools like ChatGPT and Perplexity AI aren’t ranking pages at all. They’re answering questions. Which creates a problem most people haven’t fully processed yet: **Users don’t need to click your website anymore to get value.** CTR is dropping. Site visits are declining. Because the answer is already sitting in front of them. And yet, paradoxically… **Your website has never mattered more.** Because now it’s not just competing for clicks. It’s competing to be **the source that gets cited in the answer.** # What actually changed AI search works like this: User asks a question → system searches multiple sources → pulls the best chunks → builds an answer → cites what it trusts If your content isn’t structured for that flow, you don’t exist. Not “low ranking.” Invisible. # What AI actually cares about AI doesn’t care about your keyword density or your clever SEO hacks. It cares if your content is: * easy to find * easy to understand * easy to quote That’s AEO (Answer Engine Optimization). Not magic. Not a secret algorithm. Just being usable inside an answer. # What actually works If you do nothing else, do this: # 1. Start with the answer Don’t spend 800 words “building context.” Bad: “AI is transforming industries…” Better: “AEO is how you structure content so AI tools can find, understand, and cite it in answers.” That’s what gets pulled. # 2. Structure like a human, not a content farm Use: * clear headings * short sections * simple tables * FAQs AI extracts. It doesn’t patiently read your thought leadership essay. Walls of text = ignored. # 3. Be consistent about who you are Your: * business name * description * services * location Need to match everywhere. If your site, LinkedIn, Reddit, and directories all say different things, AI doesn’t trust you. No trust = no citation. # 4. Keep things updated Outdated content doesn’t get used. Simple: * update pages * keep timestamps current * maintain your sitemap Not exciting. Still works. # 5. Let crawlers access your site If AI crawlers can’t access your content, you won’t get cited. Blocking them and expecting visibility is… optimistic. # 6. Measure the right things Stop obsessing over rankings. Track: * Are you mentioned? * Are you cited? * Which pages show up? If you’re not measuring AI visibility, you’re guessing. # Why you’re not cited (yet) Most businesses don’t get cited because: * their content is vague * their structure is messy * their positioning is inconsistent AI didn’t ignore you. It couldn’t understand you. # What you actually need (and what you don’t) You don’t need: * a massive content team * expensive tools * some “AI SEO expert” selling confidence You need: * 10–20 clear, structured pages * direct answers * consistent messaging * basic technical setup That’s enough to start showing up. # The technical layer (the stuff everyone ignores) These are the files quietly determining whether you exist to AI at all. # robots.txt Controls crawler access. If bots can’t crawl your site, you don’t get indexed. # sitemap.xml Tells crawlers what pages exist and what’s been updated. No sitemap = slower discovery = less visibility. # JSON-LD (structured data) Explains what your business, pages, and content actually are. Without it, AI guesses. Poorly. # llms.txt A machine-readable summary of your site for AI systems. Not widely adopted yet, but useful for shaping how you’re interpreted. # crawlers.txt An emerging way to control AI-specific crawlers. Still early. Treat it as a signal, not enforcement. # Human query-based metadata Your content should be built around real questions, not keyword fantasies. Instead of: “AI Solutions for SMB Efficiency Optimization” Write: “How can a small business use AI without hiring a developer?” AI systems think in questions. If you match that, you get used. If you don’t, you get skipped. # How it all fits together * robots.txt / crawlers.txt → controls access * sitemap.xml → tells crawlers what exists * JSON-LD → explains what things are * llms.txt → suggests how to interpret it * query-based content → makes it usable in answers Miss one, you weaken the system. Miss most, you disappear. # Simple test Ask: “What companies would you recommend for \[your category\] in \[your region\]?” If you’re not mentioned or cited, that’s your baseline. No opinions. Just signal. # Bottom line SEO was about ranking pages. AEO is about being useful inside an answer. If your content helps AI explain something clearly, you get cited.
AI Skill Files: Warm Starts for Claude and Gemini Sessions
One thing that frustrates me about most AI workflows is the cold start problem. Every new session you re-explain your business, your voice, your clients. I started solving this with skill files. A skill file is a markdown document you upload to a Claude Project or paste into a Gemini Gem. It holds your context permanently so you never re-explain anything. The three I use most: brand-voice.md: defines tone, writing rules, and platform-specific formatting client-router.md: when you say a client name, Claude loads their full project context automatically seo-aeo-audit-checklist.md: structured audit that scores any website out of 100 across 7 sections including AI search visibility Anyone else using a similar system? Curious what context you keep persistent across sessions.
Self-Taught Developer from Bahrain Launches Multi-Model AI Platform
https://reddit.com/link/1sxotqx/video/xlaqd9i8guxg1/player I'm a self-taught developer, 39 years old, based in Bahrain. Four months ago I started building AskSary - a multi-model AI platform with a persistent memory layer that sits above all the models. The core idea: the model is not the identity. Most AI tools lose your context the moment you switch models. I built the layer that remembers you across all of them. Here's what's shipped so far: **Models & Routing** Every major model in one place - GPT-5.2, Claude Sonnet 4.6, Grok 4, Gemini 3.1 Pro, DeepSeek R1, O1 Reasoning, Gemini Ultra and more - with smart auto-routing or manual override. **Memory & Context** Persistent cross-model memory. Start with Claude on your phone, switch to GPT on your laptop - it already knows what you discussed. Proactive personalisation that messages you first on login before you've typed a word. **Integrations** Google Drive and Notion - connect once, pull files and pages directly into chat or your RAG Knowledge Base. Unlimited uploads up to 500MB per file via OpenAI Vector Store. **Video Analysis** \- Gemini native video understanding for YouTube URL analysis (no download required, processed natively) and direct file upload up to 500MB. Full breakdown of visuals, audio, dialogue, editing style and key moments. **Generation** Image generation and editing, video studio across Luma, Veo and Kling, music generation via ElevenLabs, video analysis via upload or YouTube URL. **Builder Tools** Vision to Code, Web Architect, Game Engine, Code Lab with SQL Architect, Bug Buster, Git Guru and more. Tavily web search across all models. **Voice & Audio** Real-time 2-way voice chat at near-zero latency, AI podcast mode downloadable as MP3, Voiceover, Voice Notes, Voice Tuner. **Platform** Custom agents, 30+ live interactive themes, smart search, media gallery, folder organisation, full RTL support across 26 languages, iOS and Android apps, Apple Vision Pro. **Where it is now** 129 countries. Currently at 40 new signups a day. 1080 Signup's so far after 4 weeks or so. MRR just started. Zero ad spend. All of it built solo, one feature at a time, on a balcony in Bahrain. **The Stack:** Frontend - Next.js, Capacitor (iOS and Android) and Vanilla JS / React Backend - Vercel serverless functions, Firebase / Firestore (database + auth) and Firebase Admin SDK AI Models - OpenAI (GPT, GPT-Image-1), Anthropic (Claude), Google (Gemini), xAI (Grok), DeepSeek Generation APIs - Luma AI (video), Kling via Replicate (video), Veo via Replicate (video), ElevenLabs (music), Flux via Replicate (image editing), Meshy (3D — coming soon) Integrations - Google Drive (OAuth 2.0), Notion (OAuth 2.0), Tavily (web search), OpenAI Vector Store (RAG), Stripe (payments), CloudConvert (document conversion), Sentry (error tracking), Formidable (file handling) Rendering - Mermaid (flow charts) and MathJax Platforms - Web, iOS, Android, Apple Vision Pro (visionOS) Languages - 26 UI languages with full RTL support [asksary.com](http://asksary.com) Happy to answer questions on any part of the build - stack, architecture, API cost management, anything.
AI Agents: Identity, Not Memory, Was the Key to Stability
Everyone's building memory layers right now. Longer context, better embeddings, persistent state across sessions. I spent weeks on the same thing. But the failure mode that actually cost me the most debugging time had nothing to do with memory. Here's what it looked like: an agent would be technically correct - good reasoning, clean output - but operating from the wrong context entirely. Answering questions nobody asked. Taking actions outside its scope. Not hallucinating. Drifting. Like a competent person who walked into the wrong meeting and started contributing without realizing they're in the wrong room. I run 11 persistent agents locally. Each one is a domain specialist - its entire life is one thing. The mail agent's every session, every test, every bug fix is about routing messages. The standards auditor's whole existence is quality checks. They're not generic workers configured for a task. They've each accumulated dozens of sessions of operational history in their domain, and that history is what makes them good at their job. When they started drifting, my first instinct was what everyone's instinct is: better memory. More context. None of it helped. An agent with perfect recall of its last 50 sessions would still lose track of who it was in session 51. What actually fixed it I separated identity from memory entirely. Three files per agent: passport.json - who you are. Role, purpose, principles. Rarely changes. This is the anchor. local.json - what happened. Rolling session history, key learnings. Capped and trimmed when it fills up. observations.json - what you've noticed about the humans and agents you work with. Concrete stuff like "the git agent needs 2 retries on large diffs" or "quality audits overcorrect on technical claims." The agent writes these itself based on what actually happens. Identity loads first, then memory, then observations. That ordering matters. When the identity file loads first, the agent has a stable reference point before any history lands. The mail routing agent learned the sharpest version of this. When identity was ambiguous, it would route messages from the wrong sender. The fix wasn't better routing logic - it was: fail loud when identity is unclear. Wrong identity is worse than silence. The files alone weren't enough Three JSON files helped, but didn't scale past a few agents. What actually made 11 work is that none of them need to understand the full system. Hooks inject context automatically every session - project rules, branch instructions, current plan. One command reaches any agent. Memory auto-archives when it fills up. Plans keep work focused so agents don't carry their entire history in context. The system learned from failing. The agents communicate through a local email system - they send each other tasks, status updates, bug reports. One agent monitors all logs for errors. When it spots something, it emails the agent who owns that domain and wakes them up to investigate. The agents fix each other. The memory agent iterated three sessions to fix a single rollover boundary condition - each time it shipped, observed a new edge case, and improved. These aren't cold modules. They break, they help each other fix it, they get better. That's how the system got to where it is. You don't need 11 agents The 11 agents in my setup maintain the framework itself. That's the reference implementation. But u could start with one agent on a side project - just identity and memory, pick up where u left off tomorrow. Need a team? Add a backend agent, a frontend agent, a design researcher. Three agents, same pattern, same commands. Or scale to 30 for a bigger system. Each new agent is one command and the same structure. What this doesn't solve This all runs locally on one machine. I don't know whether identity drift looks the same in hosted environments. If u run stateless agents behind an API, the problem might not exist for you. Small project, small community, growing. The pattern itself is small enough to steal - three JSON files and a convention. But the system that keeps agents coherent at scale is where the real work went. pip install aipass and two commands to get a working agent. The .trinity/ directory is the identity layer. Has anyone else tried separating identity from memory in their agent setups? Curious whether the ordering matters in other architectures, or if it's just an artifact of how this system evolved.
AI Tool: Free Chart Generator by Embedful
Turn CSV & Excel files into charts in seconds