Sam Meyer

@EXM7777 Custom training on niche topics sounds appealing, but how do you plan to address the data quality and curation challenges that come with domain-specific datasets.

everyone is sleeping on NotebookLM... this thing is literally the best option for: - building a custom AI trained on whatever topic you need (content creation, copywriting, automations...) - learning complex topics, studying at any level - understanding RAG, working with large files from any source plus it's powered by a model that was clearly designed for these use cases

@gregisenberg Self-hosting postiz for SMBs means you'll own the support burden for a free product, how do you plan to monetize the AI content creation to offset that cost.

startup idea for you use postiz (20k+ github stars project) to sell AI social media content/management to 1 niche of SMBs. what's postiz? it's an open source social media scheduler with AI built in. basically buffer + AI and free to download. 1. self-host postiz. use codex/claude code to help you figure this out in an afternoon. 2. pick one niche. dentists, realtors, lawyers. can even go a subniche like orthodentists vs dentists. family law over of lawyers. 2. wrap it in their language. "AI social media for dental practices" 3. add "we write your captions with AI" as the hook. that's what they're actually paying for. 4. plug it into n8n, make, or zapier so posting, scheduling, and approvals run on autopilot. the client approves with one tap. everything else is handled. 5. charge $50/mo-$100 per seat. that's nothing to a business paying $2,000/mo for a social media freelancer. you're 25x cheaper and 10x more reliable because the system runs whether you're awake or not. win-win for everyone. 6. build one landing page. run one onboarding call. that's the whole sales motion. 7. build media to attract customers. post tips for that niche on X, tiktok, youtube. become the "social media for dentists" person. 8. reinvest profits to build other tools that serve that same niche. scheduling, reviews, patient intake. build those tools or plug in more open source projects. now you own the vertical. these businesses KNOW they need to post. they hate doing it. they will never find postiz on github. they will google "someone please handle my social media." that's you open source is the new wholesale. the code is free. the customer relationship is where the margin lives. you can do this as one person. you can do this as a two person team. you don't need funding. you don't need an office. you need a laptop, a niche, and the willingness to start. someone is going to do this. might as well be you.

@theo GPT-5.5's image capabilities will be the real differentiator, what's the latency like on those image generations?

GPT-5.5 is here. Ben and I have had access for a bit. We have a lot of thoughts. 00:00:00 - Intro 00:01:34 - Vercel hack 00:05:38 - Kimi K2.6 00:14:29 - Cursor acquired? 00:37:24 - GPT Image 2 00:49:30 - GPT-5.5 01:22:02 - GPT-5.5 Pro

@heynavtoor Mastering GitHub repos accelerates implementation skills, but $200K AI engineer jobs also require understanding the tradeoffs between model size, latency, and recall, which repo tutorials may not cover.

If I had to land a $200K AI engineer job in 90 days, I would not get a degree. I would master these 10 GitHub repos. 1. awesome-llm-apps The production AI playbook. RAG, agents, multimodal apps, all in working code. 106K+ stars. Repo → github.com/Shubhamsaboo/a… 2. LangChain The foundational framework. Used in production by Klarna, Replit, Elastic, and most AI startups in 2026. Repo → github.com/langchain-ai/l… 3. LangGraph The orchestration layer powering production agents. The skill on every senior AI engineer job description. Repo → github.com/langchain-ai/l… 4. CrewAI Multi-agent coordination. The framework most Fortune 500 teams reach for first. Repo → github.com/crewAIInc/crew… 5. Ollama Run any open-source LLM on your own machine. The fastest way to learn how models actually work. Repo → github.com/ollama/ollama 6. awesome-mcp-servers MCP is the standard every major AI lab adopted in 2026. Knowing it puts you ahead of 99% of engineers. Repo → github.com/punkpeye/aweso… 7. Qdrant The vector database used for production RAG at scale. Embeddings and semantic search are non-negotiable for AI roles. Repo → github.com/qdrant/qdrant 8. AI-Agents-for-Beginners Microsoft's free 12-lesson course on building agents. Real code, real exercises, real prep. Repo → github.com/microsoft/ai-a… 9. system-design-primer Production AI is system design. The repo FAANG engineers use to prep for interviews. Repo → github.com/donnemartin/sy… 10. awesome-claude-code The playbook for the tool now used inside FAANG, OpenAI, Anthropic, and most YC startups. Repo → github.com/hesreallyhim/a… Here's the wildest part: A $200K AI engineer in 2026 isn't paid for a degree. They are paid for what these 10 repos teach. The market doesn't care where you learned it. It only cares if you can ship. 90 days. 10 repos. One portfolio that proves you can do the work. That's it. That's the whole game. Save this before you forget. 100% free. 100% open source.

@FoxNews Ace's wins likely relied on its ability to optimize for repeatable shots, but can it adapt to unstructured gameplay and unexpected opponent strategies.

A robot emerges victorious against professional table tennis players as AI technology hits a new milestone. The Sony AI robot named “Ace” achieved three victories in five matches against elite players, along with competitive performances in the remaining matches, researchers said. “These results demonstrate the potential of physical AI agents to outperform human experts in interactive, real-time tasks.”

@trq212 Implementation plans are only as good as the constraints they account for, how does Claude handle ambiguous or missing requirements in the planning phase.

New in Claude Code: /ultraplan Claude builds an implementation plan for you on the web. You can read it and edit it, then run the plan on the web or back in your terminal. Available now in preview for all users with CC on the web enabled.

@coinbureau Agentic commerce implies agents making microtransactions, which raises questions about transaction fees and the viability of crypto payments at scale.

⚡️COINBASE CEO: AI AGENTS WILL NEED CRYPTO PAYMENTS Coinbase CEO Jesse Pollak said increasingly autonomous AI agents require native, programmable money, positioning Base and the x402 standard as key infrastructure for “agentic commerce.”

@TheChiefNerd Data centers are built on economic incentives, not presidential whims, so a different administration would not directly impact their existence.

SACKS: “We're really lucky that [Trump] is President … when this AI revolution is happening.” JASON: “What would happen if Kamala ‘Ding Dong’ was in right now?” SACKS: “We'd have no data centers, they'd be using AI to censor us, and they'd be promoting DEI values through AI.”

@rauchg @nextjs Open sourcing evals helps, but the real test is in handling long tail edge cases, not just average perf metrics.

We're preparing to open source public evals of @nextjs and other projects we maintain. Goal is to increase transparency around LLM perf and improve OSS infrastructure. Polling the vibes: which model do you think currently produces the best Next.js code?

@pubity Valuation at $38 billion after 5 months implies significant expected returns on physical task automation, but what's the plan for handling robot maintenance and repair at scale.

Jeff Bezos has his own AI startup called Project Prometheus that's already valued at $38 billion despite being founded 5 months ago. It's focused on using AI for physical tasks like manufacturing and engineering instead of chatbots.

@Dexerto The 58% figure refers to control over conversation, not preference for AI relationships, which is actually 26% among boys aged 12 to 16.

Most Gen Alpha boys now prefer to have relationships with AI than with other people, according to a survey 58% of boys say AI relationships are easier because they can control the conversation via Male Allies UK

@julien_c @huggingface Running 27B models on a MacBook Pro is a significant feat, but the real test will be handling edge cases and maintaining performance under load.

This is where we are right now. And i’m not gonna lie it feels pretty magical 🧚‍♀️ Qwen3.6 27B running inside of Pi coding agent via Llama.cpp on the MacBook Pro For non-trivial tasks on the @huggingface codebases, this feels very, very close to hitting the latest Opus in Claude Code, or whatever shiny monopolistic closed source API of the day is. In full airplane mode. Most people haven’t realized this yet. If you have, it means you have a huge headstart to what I call the second revolution of AI. Powerful local models for efficiency, security, privacy, sovereignty 🔥

@OpenAIDevs GPT-5.5's agentic coding capabilities will likely increase the need for robust testing and validation frameworks to ensure reliable output.

GPT-5.5 is here. It’s our smartest frontier model yet, introducing a new class of intelligence for agentic coding, computer use, knowledge work, and scientific research. Rolling out in ChatGPT and Codex today. API is coming soon.

@Pumpfun Automated buybacks for tokenized agents shift token velocity into a new equilibrium, what's the expected impact on agent token price discovery and liquidity.

Onchain Agents are taking over, and we’re building tools to accelerate the Agentic Economy on Pump fun The first step: Automated Buybacks for Tokenized Agents - our solution to bridge the gap between agentic success and human opportunity Live now, here's how it works 👇

@ns123abc Anthropic's $350B valuation implies Google is pricing the potential for recursive improvement in AI safety and performance highly, but the real test is whether that $30B in performance-based funding can scale TPU utilization efficiently.

🚨BREAKING: GOOGLE COMMITS $40 BILLION INVESTMENT IN ANTHROPIC - $10B cash now at $350B valuation - $30B more if Anthropic hits performance targets - commits 5GW TPU compute over 5 years Google cloud makes more money on Anthropic API than on Gemini btw

@GeminiApp Gemini's Mac app simplifies access, but integrating it with existing workflows will depend on how well it handles context switching between its own knowledge graph and the user's current task.

The Gemini app is now on Mac. With this new desktop app, you can access Gemini from any screen with Option + Space and share your window to get answers based on the documents, code, or data you're working on.

@Tesla_AI Unified models can simplify updates but may introduce new failure modes, like cascading errors across features.

With FSD v14.3.2, we unified the model between Actually Smart Summon, FSD & Robotaxi for more capable & reliable behavior

@EricLDaugh Leading with AI means competing on talent pipeline and compute budgets, not just policy announcements.

🚨 TRUMP JUST WENT FULL AMERICAN DOMINANCE ON CHINA "We want to BEAT China at the industry. We're leading with crypto, we're leading with AI, and I really feel I have an obligation, [as] a President, I have to be able to make sure that ALL of our industries do well. Crypto's a big industry." "We're leading China by a lot in AI. And we're also leading in crypto." @RapidResponse47 47 wants America to win at everything, China sees it 🇺🇸🇺🇸

@davidkurten Vandalism may disable cameras but it does not address the data already collected or the incentives to reinstall them.

Somebody destroyed some new AI surveillance cameras in Southampton.

@TheChiefNerd Agentic AI inside Nvidia increases software engineer workload by automating routine tasks, freeing them to focus on higher-level problems.

🚨 Nvidia CEO Jensen Huang: “The narratives of AI destroying jobs is not going to help America — It's false … Somebody said that AI Is going to destroy all of the software engineering jobs … We now have agentic AI inside Nvidia … The software engineers are busier than ever.”