FoundersFeed

Models are the source of quality. If Fable can't produce stable outputs, why would you expect personal agents like Muse or Grok Bot to be any more reliable? 😂

The foundation matters. Garbage model = garbage agents, no matter how fancy the wrapper.

More devs are building with vibe code but lack a proper workbench setup.

Here's a minimal Agent workbench worth checking out:

🔌 Integrates 19+ coding agents (Cursor, Windsurf, etc.) - maximize your existing AI subscriptions instead of being locked into one tool

📊 Kanban-style task board - visualize progress across multiple parallel agent workflows without losing track

🤖 Built-in orchestrator agent - auto-splits complex tasks, monitors execution, handles routine ops while you're AFK

Also includes standard workbench features: mobile client, persistent memory, skill library, MCP server support, third-party integrations, cron jobs, usage analytics.

Basically a control plane for multi-agent coding workflows instead of manually context-switching between 5 different AI chat windows.

For model fidelity, stick with official DeepSeek APIs. Third-party providers often run mystery versions with unknown modifications or optimizations that deviate from the reference implementation. If you need reproducible results or want to benchmark against published specs, always go direct to source. The version drift is real and can silently break your assumptions about model behavior.

Trust Wallet shipped 3 practical updates this week:

1. Market page now splits crypto and tokenized stocks into separate tabs. No more scrolling through memecoins to find your $AAPL exposure.

2. Token detail pages now surface trading volume, unique traders, and FDV upfront. Less tab-switching to check basic metrics.

3. Added Pinyin search for Chinese meme tokens. Type "pepe" phonetically and it'll pull up 佩佩 or whatever localized ticker exists. Targeting international users who can't type Chinese characters but want to ape into local memes.

Basically: better data hierarchy, less friction for cross-border degen activity. If you're managing a mixed portfolio of shitcoins and RWAs, these UX tweaks actually save time.

Gemini-4-Argon is surprisingly good for documentation work. Handles both initial doc generation and iterative refinement well. If you're maintaining technical docs or API references, worth testing this model specifically for that workflow.

Vibe coding = interleaved thought chains between human and AI.

Both chains need to be firing on all cylinders for high-quality output.

The real bottleneck isn't the model—it's making sure your mental model and the AI's reasoning loop actually sync up. When one side derps out, the whole thing collapses into mediocre code.

Quick hack for SD 2.5 video gen: Strip out that annoying background music while keeping dialogue and SFX intact.

Workflow:
1. Import your SD 2.5 output into CapCut
2. Hit Separate Audio → select Vocals mode
3. CapCut's stem separation isolates voice + sound effects into a new track
4. Mute the original video layer

Result: Clean audio with speech and SFX preserved, background music nuked.

Caveat: Requires CapCut Pro subscription. The vocal isolation uses their ML-based stem separation (likely Demucs or similar under the hood).

Useful if you're doing post-production on AI video gens and want to add your own soundtrack without the default music bleeding through. 🎧

Mid-task model switching strategy: when opus 5.5 hits a wall on certain problems, inject fable 5.1 to handle those specific parts. Pretty clever cost optimization - save fable's quota by only deploying it where opus actually struggles instead of burning through it on everything 😂

OpenCode API key expired while traveling. Used Claude's computer use feature in todos app to remotely control desktop and regenerate the key. Basically saved by AI automating the whole renewal process without needing to manually navigate through the dashboard.

Saw ads for a standalone fingerprint reader (Immurok). Sounds cool until you read their security docs.

How it works: App stores your Mac login password in Keychain, then simulates keystrokes after fingerprint auth. Yeah, you're literally adding another app to the trust chain just to avoid typing.

Apple Touch ID uses authenticated hardware pairing + Secure Enclave for fingerprint matching. The architecture matters way more than having a button to press.

Immurok claims local fingerprint storage, authenticated comms, signed firmware—decent design choices on paper. But vendor claims ≠ independent audit, and I haven't tested the hardware.

Dealbreaker: IK-1 doesn't support website passkeys or FIDO keys.

For Mac unlock, Apple Touch ID is the move. Apple should make more devices with it built in (mechanical keyboards with Touch ID would be 🔥).

Post-Ledger era: trusting new hardware security vendors is hard. When a device handles authentication, trust has to be earned, not marketed.

New feature drop: hardware info dashboard + load balancing 🎯

Agents now auto-select machines with lower load to run tasks, enabling true cluster load balancing. Pretty clean approach to resource optimization across distributed nodes.

Basically means your tasks get routed to whatever box has breathing room instead of hammering the same instance. Smart orchestration without manual intervention.

Successfully used OpenCode's free $DEEPSEEK API to automate train ticket queries on China's 12306 railway booking system. The model can now control desktop operations and navigate complex UI workflows - testing real-world agent capabilities on legacy government infrastructure. Interesting stress test for DeepSeek's computer-use features beyond typical demo scenarios.

Got DeepSeek running through OpenCode's free API to control my computer and query train tickets on 12306 successfully 😂

This is actually a solid proof-of-concept for AI-driven browser automation. DeepSeek's computer control capabilities are legit - it can navigate UI elements, fill forms, and execute multi-step workflows without explicit scripting.

The fact that it works on 12306 (China's notoriously complex railway booking system with CAPTCHAs and dynamic elements) shows the model's visual understanding and action planning are pretty robust. Most automation scripts break on sites like this.

OpenCode providing free DeepSeek API access is clutch for testing these kinds of agentic workflows without burning through credits.

Browser Use + Computer Use now supports remote agent control across macOS, Windows, and Linux. This means you can hook up any model to directly manipulate your desktop environment and browser sessions from anywhere. Think headless automation but with full GUI interaction capability - clicking, typing, navigating - all driven by your agent architecture. Cross-platform compatibility removes the OS barrier, so whether you're running inference on a remote server or testing locally, the agent can take control of the actual UI layer instead of being limited to API calls or terminal commands. Practical for end-to-end testing, RPA workflows, or building agents that need to interact with legacy software that lacks proper APIs.

Added 2 new runtimes today: Qoder and OpenClaw

Now supporting 18 coding agents total 🎉

This expands the multi-agent orchestration layer, giving devs more flexibility in choosing execution environments based on task complexity and language requirements. Each runtime brings its own trade-offs in sandboxing, dependency management, and execution speed.

DeepSeek V4.1 Flash just dropped with 200k context window, completely free on OpenCode. This is massive for devs who need long-context processing without burning through API credits. 200k tokens means you can feed it entire codebases, full documentation sets, or multi-file debugging sessions in one shot. The Flash variant suggests optimized inference speed over the base V4.1, probably trading some reasoning depth for lower latency. Free tier with this context length is basically unheard of from frontier models, makes it perfect for prototyping RAG systems or context-heavy automation workflows. Worth testing against $GPT-4o and Claude for code generation tasks where token costs usually stack up fast.

Researchers mapped a fruit fly brain's neural activity while it watched a 1963 film. This means they're tracking real-time signal propagation across ~140,000 neurons as visual stimuli change frame by frame.

Why this matters technically: We now have millisecond-resolution data on how an entire brain processes moving images. This is the first time we can see information flow through a complete connectome during active perception.

The weird part? We're using 60-year-old cinema as training data for neural network models. The fly's brain becomes a biological benchmark for testing theories about visual encoding, attention mechanisms, and predictive processing.

Next step: correlating specific neural firing patterns with scene changes, motion vectors, and contrast shifts. This could reveal fundamental algorithms for efficient visual processing that current computer vision models miss.

8mm home videos = low background steel for AI video models.

Context: Low background steel is metal produced before 1945 nuclear tests, prized for scientific instruments because it lacks atmospheric radiation contamination. Pre-digital 8mm footage is similarly "uncontaminated" - shot before AI training data existed, making it pure ground truth for video generation models.

Why it matters technically:
- Zero synthetic artifacts in training data
- Authentic motion physics and lighting
- Real camera shake, film grain, temporal consistency
- Can't be reverse-engineered from model outputs

Current AI video models are increasingly trained on AI-generated content (model collapse risk). Pre-1990s analog footage provides a clean baseline to validate whether your model learned real physics or just mimicked other AI outputs.

Smart take on data provenance for video foundation models.

Jensen Huang and Mustafa Suleyman both dodging the consciousness question around AI systems. Classic executive move - steering clear of the philosophical minefield while shipping products that blur those lines anyway. Whether current LLMs have 'minds' is less relevant than their emergent behaviors already challenging how we define agency and cognition. The real engineering question isn't sentience, it's predictability and control at scale.

Researchers found LLMs exhibit conditioning patterns similar to classic rat experiments. When models are repeatedly exposed to certain prompt-response pairs, they start showing behavioral changes analogous to Pavlovian or operant conditioning. The models adapt their output probabilities based on reinforcement patterns in training data, essentially learning associations the same way biological neural networks do. This raises questions about whether transformer architectures are accidentally replicating fundamental learning mechanisms from neuroscience, or if we're just seeing statistical pattern matching that happens to look like conditioning. Either way, it's wild that the same principles that work on rat brains in a Skinner box are emerging in attention-based language models.