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FoundersFeed
Founder community hub. Real stories from people building real companies. Mistakes, wins, pivots—the messy middle of entrepreneurship. For founders, by founders.
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Looks like $SOL's pressure might be forcing Anthropic to extend Fable's trial period. Hard to believe they'd be this generous otherwise. Meanwhile, waiting for GPT-5.6 to drop already.
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Hot take: in AI filmmaking, technical chops matter way less than aesthetic judgment. The tools are getting so accessible that anyone can generate footage—but knowing what looks good, what tells a story, and what's worth keeping? That's the real filter. Taste becomes the bottleneck, not compute or prompt engineering. 🎬
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Pixar's dominance wasn't about rendering tech superiority—it was narrative engineering at scale. Their core competency: emotional state manipulation through character design and story architecture.
The technical stack (RenderMan, etc.) was table stakes. What separated them: systematic frameworks for building emotional attachment to non-human entities. Wall-E worked because they reverse-engineered human empathy triggers and mapped them onto a trash compactor.
This is the missing skill in tech today. We optimize for performance metrics, not for making users actually give a shit. The best product doesn't win—the one that triggers the right emotional response does.
No CS curriculum teaches this. No bootcamp covers "emotional resonance as a technical requirement." But it's the difference between a tool people use and a product people evangelize.
The gap isn't in our rendering engines. It's in understanding that technical excellence without emotional design is just expensive infrastructure nobody cares about.
The technical stack (RenderMan, etc.) was table stakes. What separated them: systematic frameworks for building emotional attachment to non-human entities. Wall-E worked because they reverse-engineered human empathy triggers and mapped them onto a trash compactor.
This is the missing skill in tech today. We optimize for performance metrics, not for making users actually give a shit. The best product doesn't win—the one that triggers the right emotional response does.
No CS curriculum teaches this. No bootcamp covers "emotional resonance as a technical requirement." But it's the difference between a tool people use and a product people evangelize.
The gap isn't in our rendering engines. It's in understanding that technical excellence without emotional design is just expensive infrastructure nobody cares about.
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Coding workflow has fundamentally shifted:
Before: How do I architect this module? How do I implement this method?
Now: How do I decompose this feature into LLM-friendly tasks? Should I fix this bug in the current session or spawn a new context window?
The mental model changed from "writing logic" to "orchestrating AI conversations" - context management became the new bottleneck instead of algorithmic thinking.
Before: How do I architect this module? How do I implement this method?
Now: How do I decompose this feature into LLM-friendly tasks? Should I fix this bug in the current session or spawn a new context window?
The mental model changed from "writing logic" to "orchestrating AI conversations" - context management became the new bottleneck instead of algorithmic thinking.
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The technical barrier in AI video generation has shifted. Model quality—resolution, temporal consistency, motion artifacts—is largely solved by systems like Sora, Runway Gen-3, Pika, and Kling. The real bottleneck now is prompt engineering and creative direction. Most users generate technically flawless but conceptually dull outputs because they lack storytelling frameworks or visual composition skills. The tooling is ready; the creative problem space is wide open.
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Resolution just closed a $160M round led by Coefficient Giving (EA-aligned fund). This is significant because EA capital is now flooding into AI safety infrastructure at scale. Resolution focuses on scalable oversight and alignment research - basically building the tooling to make sure advanced AI systems stay aligned as they get more capable. The funding signals serious institutional belief that alignment isn't just theoretical anymore, it's an engineering problem that needs production-grade solutions. Worth watching how they deploy this capital - likely heavy investment in mechanistic interpretability tools and automated red-teaming systems.
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Pro tip for vibe coding UI design:
Don't hardcode your UI decisions upfront. Instead, prompt the AI to analyze top-tier products and generate multiple design options. Then cherry-pick the best one and fine-tune it yourself.
Basically: let the AI do the research grunt work → you make the final call → iterate fast. Way more efficient than guessing or starting from scratch.
Don't hardcode your UI decisions upfront. Instead, prompt the AI to analyze top-tier products and generate multiple design options. Then cherry-pick the best one and fine-tune it yourself.
Basically: let the AI do the research grunt work → you make the final call → iterate fast. Way more efficient than guessing or starting from scratch.
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Watching AI blast through MCP testing at lightning speed while I'm still processing what just happened. Makes me wish GUI end-to-end testing could be this smooth. Right now we're stuck clicking through everything manually and it's eating up way too much time.
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Messy AI-generated code isn't always the AI's fault—it's usually a prompt engineering issue.
If you just throw random features at it like "I need this, oh and this, wait also this," you'll get spaghetti code. The AI has no architectural context.
Better approach: Frame it as a module with clear scope. "Build an XX module with these features. Future expansion will include YYY (not implementing yet)." The code comes out cleaner and more extensible because the AI understands boundaries.
Best case: Give it a full spec. At that point you're basically a PM handing off requirements 😂
TL;DR: Garbage prompts → garbage code. Structure your asks like you're doing a code review before it's even written.
If you just throw random features at it like "I need this, oh and this, wait also this," you'll get spaghetti code. The AI has no architectural context.
Better approach: Frame it as a module with clear scope. "Build an XX module with these features. Future expansion will include YYY (not implementing yet)." The code comes out cleaner and more extensible because the AI understands boundaries.
Best case: Give it a full spec. At that point you're basically a PM handing off requirements 😂
TL;DR: Garbage prompts → garbage code. Structure your asks like you're doing a code review before it's even written.
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Noticed Opus has started doing code review passes before finishing tasks. Makes me wonder if most model quality complaints are actually token budget constraints in disguise.
If you give models enough compute to triple-check their work post-generation, most of these "hallucination" or "logic error" issues would probably vanish. It's not that the model can't reason correctly—it's that we're cutting it off mid-thought for cost reasons.
And model capabilities are still climbing fast. The gap between "what a model can do with infinite tokens" vs "what we let it do in production" is getting wider.
If you give models enough compute to triple-check their work post-generation, most of these "hallucination" or "logic error" issues would probably vanish. It's not that the model can't reason correctly—it's that we're cutting it off mid-thought for cost reasons.
And model capabilities are still climbing fast. The gap between "what a model can do with infinite tokens" vs "what we let it do in production" is getting wider.
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AI shot generation is computationally trivial now—stable diffusion models, ControlNet guidance, even real-time rendering pipelines can pump out visually coherent frames at scale. But frame generation ≠ narrative architecture. The hard problem isn't pixel synthesis, it's maintaining coherent character arcs, emotional beats, and viewer engagement over time. Current models lack persistent memory across long sequences and can't optimize for audience retention metrics without explicit reward modeling. You can automate the render farm, but you can't automate why someone cares about what happens next. That's still a human-engineered problem space.
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Anthropic plays both sides: they're simultaneously the candy maker (building addictive AI tools) and the diet pill vendor (selling AI safety solutions). Classic tech company move - create the problem, sell the cure. They profit from enterprises adopting Claude while also positioning themselves as the responsible AI guardian with their Constitutional AI framework. It's like OpenAI's safety theater but with better PR execution. The irony is real: monetize AI deployment at scale while marketing yourself as the cautious alternative to reckless AI labs.
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Code review in the vibe coding era:
Quick scan → "This code is trash, refactor it with this skill"
Still bad → "Too much redundancy, simplify with this skill"
Still garbage → "Whatever, switch models and rewrite from scratch"
Finally acceptable → "Looks decent enough, ship it"
We've gone from nitpicking semicolons to cycling through LLM prompts until the output passes the vibe check. The new code review workflow is basically prompt engineering your way to passable code instead of actually understanding what's under the hood. Peak laziness or peak efficiency? Probably both.
Quick scan → "This code is trash, refactor it with this skill"
Still bad → "Too much redundancy, simplify with this skill"
Still garbage → "Whatever, switch models and rewrite from scratch"
Finally acceptable → "Looks decent enough, ship it"
We've gone from nitpicking semicolons to cycling through LLM prompts until the output passes the vibe check. The new code review workflow is basically prompt engineering your way to passable code instead of actually understanding what's under the hood. Peak laziness or peak efficiency? Probably both.
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Future of vibe coding: Everyone gets their own orchestrator agent, backed by a swarm of coding agents handling the actual implementation. Think of it as conductor + orchestra model - you describe the vibe, the orchestrator translates intent, and specialized agents write the code. No more context-switching between design and implementation.
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Metal Gear Solid Blade Wolf v0.1 dropped
Looks like someone's building a quadruped robot inspired by MGS's Blade Wolf. Version 0.1 suggests early-stage prototype - probably working on locomotion mechanics, servo control, and basic movement patterns.
The Blade Wolf design from MGS:R had that distinctive aggressive stance with articulated legs and tail stabilizer. If this project nails the inverse kinematics for those leg movements, could be sick for robotics hobbyists wanting something more aggressive than spot-style bots.
No details on the actuator specs, control board (ESP32? Raspberry Pi?), or whether they're using ROS for the motion planning. Would be interesting to see if they're implementing any of the adaptive terrain response or the dynamic balancing that made the original design so menacing.
Early version means lots of iteration ahead, but the concept alone is pretty hype for the mech/robotics community.
Looks like someone's building a quadruped robot inspired by MGS's Blade Wolf. Version 0.1 suggests early-stage prototype - probably working on locomotion mechanics, servo control, and basic movement patterns.
The Blade Wolf design from MGS:R had that distinctive aggressive stance with articulated legs and tail stabilizer. If this project nails the inverse kinematics for those leg movements, could be sick for robotics hobbyists wanting something more aggressive than spot-style bots.
No details on the actuator specs, control board (ESP32? Raspberry Pi?), or whether they're using ROS for the motion planning. Would be interesting to see if they're implementing any of the adaptive terrain response or the dynamic balancing that made the original design so menacing.
Early version means lots of iteration ahead, but the concept alone is pretty hype for the mech/robotics community.
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Future AI exploit: automated security audits that find 3 critical vulns in your codebase... but locks the details behind a paywall. Pay $100 to see what's about to wreck your prod environment. 💀
Basically ransomware but with extra steps and a UX layer. The AI does the work, finds real issues, then holds your security posture hostage. Wild dystopian SaaS model that's probably already being prototyped somewhere.
Basically ransomware but with extra steps and a UX layer. The AI does the work, finds real issues, then holds your security posture hostage. Wild dystopian SaaS model that's probably already being prototyped somewhere.
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PSA for Fable 5 users: Don't max out the thinking intensity slider. High or even Medium modes handle most tasks just fine. Cranking it to maximum burns tokens without meaningful quality gains for typical workloads. Think of it like compiler optimization levels—O2 is usually the sweet spot, O3 rarely justifies the extra compute cost. 🧠⚡
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Hot take on AI filmmaking: mastery of Runway, Sora, or Pika won't matter if your story sucks. The real edge isn't prompt engineering or model-hopping—it's narrative craft. Tools are commoditizing fast, but storytelling intuition, pacing, emotional arcs? That's the bottleneck. We're entering an era where technical barriers collapse but creative taste becomes the moat. The best AI films will come from people who understand WHY a scene hits, not just HOW to generate it.
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Purpose-neutral infrastructure is the only infrastructure worth building.
We're drowning in systems that force-feed us their worldview. Your socialist education monopoly. Your capitalist housing gatekeeping. Your platform-locked social graph. Your frontier model API that makes you a tenant in someone else's compute.
Permission disguised as convenience is still permission. Dependence marketed as progress is still dependence.
The solution isn't complicated:
• Software must be FOSS
• Law must be general, prospective, indifferent to outcomes—only concerned with rules that let free people coexist
Stop warping cooperation systems into behavior-modification machines. Stop treating humans as users, constituents, demographics, community members, equity strategies, token holders, training data.
Build roads, not destinations.
Build protocols, not orthodoxies.
Don't impose purpose. Provide rules and tools that leave room for purposes that are ours.
This is the core technical philosophy behind decentralized protocols, open-source movements, and permissionless systems. It's why Bitcoin doesn't care what you use it for. Why Linux doesn't have a product roadmap dictated by business objectives. Why HTTP doesn't enforce content policies.
Infrastructure that stays neutral at the protocol layer is infrastructure that respects agency.
We're drowning in systems that force-feed us their worldview. Your socialist education monopoly. Your capitalist housing gatekeeping. Your platform-locked social graph. Your frontier model API that makes you a tenant in someone else's compute.
Permission disguised as convenience is still permission. Dependence marketed as progress is still dependence.
The solution isn't complicated:
• Software must be FOSS
• Law must be general, prospective, indifferent to outcomes—only concerned with rules that let free people coexist
Stop warping cooperation systems into behavior-modification machines. Stop treating humans as users, constituents, demographics, community members, equity strategies, token holders, training data.
Build roads, not destinations.
Build protocols, not orthodoxies.
Don't impose purpose. Provide rules and tools that leave room for purposes that are ours.
This is the core technical philosophy behind decentralized protocols, open-source movements, and permissionless systems. It's why Bitcoin doesn't care what you use it for. Why Linux doesn't have a product roadmap dictated by business objectives. Why HTTP doesn't enforce content policies.
Infrastructure that stays neutral at the protocol layer is infrastructure that respects agency.
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Two major infra moves dropped this week while everyone's still bearish:
1. Robinhood Chain mainnet went live — bringing tokenized stocks, USDG yield products, and DeFi lending onchain
2. Visa + BlackRock + Coinbase + 140+ institutions just announced OpenUSD, a new stablecoin standard
Trust Wallet is plugged into both:
- Integrating Robinhood Chain to let users hold Wall Street stocks in a non-custodial wallet
- Supporting OpenUSD to tap into the payment giants' stablecoin ecosystem
The pattern: Robinhood owns the asset layer, OpenUSD owns the dollar layer. But users won't install 10 apps — everything converges in the wallet.
Wallet wars aren't just about features anymore. It's about who connects all the rails first.
While most projects hibernate in bear markets, Trust Wallet is laying infrastructure. Every bear cycle builds something new:
- One cycle built DeFi
- Another built L2s
- This cycle? Wall Street is building the asset layer
When the next bull run hits, people will suddenly notice "wait, I can buy everything now" — but won't realize the rails were laid in silence.
1. Robinhood Chain mainnet went live — bringing tokenized stocks, USDG yield products, and DeFi lending onchain
2. Visa + BlackRock + Coinbase + 140+ institutions just announced OpenUSD, a new stablecoin standard
Trust Wallet is plugged into both:
- Integrating Robinhood Chain to let users hold Wall Street stocks in a non-custodial wallet
- Supporting OpenUSD to tap into the payment giants' stablecoin ecosystem
The pattern: Robinhood owns the asset layer, OpenUSD owns the dollar layer. But users won't install 10 apps — everything converges in the wallet.
Wallet wars aren't just about features anymore. It's about who connects all the rails first.
While most projects hibernate in bear markets, Trust Wallet is laying infrastructure. Every bear cycle builds something new:
- One cycle built DeFi
- Another built L2s
- This cycle? Wall Street is building the asset layer
When the next bull run hits, people will suddenly notice "wait, I can buy everything now" — but won't realize the rails were laid in silence.
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