I was banned from the homeschool community for asking this question about AI. 🤦🏼‍♀️

Any opinions about education and what our kid should really be focusing on?

WRIT-FM is a 24/7 talk radio station where Claude generates all spoken content. Live at radio.khy.io, source at github.com/keltokhy/wvoid-fm.

Technical breakdown:

The system splits cleanly into two layers: AI generation and deterministic plumbing.

Claude CLI (claude -p) receives persona prompts for 5 distinct hosts — each defined with identity, voice style, philosophy, and explicit anti-patterns (things the host would never say). It generates 1,500-3,000 word scripts for 7 segment types: deep dives, simulated interviews, panel discussions (two AI hosts debating), news analysis (fed real RSS headlines), stories, music essays, and listener mailbag. Kokoro TTS renders scripts to audio, chunking long segments at sentence boundaries and concatenating via ffmpeg.

The streamer (stream_gapless.py) is pure heuristic — no AI at runtime. It resolves the active show from a schedule.yaml lookup (8 shows across the week), plays talk segments from a per-show queue, inserts AI-generated music bumpers (ACE-Step) between them, and deletes segments after playing. Daemon scripts poll segment counts and trigger generation when inventory drops below threshold. Play history in SQLite prevents repeats within a 4-hour window.

Architecture: single Python process pipes decoded PCM through a persistent ffmpeg encoder to Icecast. The API server runs as a daemon thread in the same process. A bash CLI (writ) manages all components via tmux sessions.

Limitations: TTS quality is the bottleneck — Kokoro is fast but occasionally stumbles on unusual phrasing. Multi-voice segments (panels, interviews) have noticeable speaker transitions. Claude sometimes generates scripts that are too short and get rejected by the word-count quality gate, requiring a retry. Music bumpers from ACE-Step vary wildly in quality.

Lessons: keeping AI out of the runtime loop was the key design decision. Pre-generating content into filesystem queues that the streamer consumes means the stream never stalls waiting for an API call. The persona anti-patterns (explicit "NEVER do X" lists) matter more than the positive identity prompts for keeping hosts consistent.

Stack: Python, ffmpeg, Icecast, Claude CLI, Kokoro TTS, ACE-Step. Runs on a Mac Mini.

Repo: github.com/keltokhy/writ-fm

Listen: https://www.khaledeltokhy.com/claude-show (free, nothing to sign up for)

Report discussing the very real enterprise AI contradiction:

• 74% of enterprises report positive AI returns
• 95% of enterprise AI pilots fail to deliver measurable P&L impact

So apparently both things can be true at once. A lot of companies seem to be counting “time saved,” internal excitement, or pilot-level wins as ROI, while far fewer are getting real financial impact at scale.

Some of the more interesting numbers in this report:

• only 5% of orgs are achieving substantial measurable AI value at enterprise scale
• while 78% of companies use AI in at least one function, only 39% report measurable EBIT impact
• average return can reach 3.7x per $1 invested, but usually only after 18 months
• one of the clearest success patterns is workflow redesign + leadership visibility
• one of the clearest traps is mistaking productivity theater for actual business outcomes

I sketched a cow and tested how different models interpret it into a realistic image for downstream 3D generation, turns out some models still lag a bit in accuracy 😄

Hey everyone, I just sent the 26th issue of AI Hacker Newsletter, a weekly roundup of the best AI links and discussions around from Hacker News. Here are some of the links:

• Coding agents could make free software matter again - comments
• AI got the blame for the Iran school bombing. The truth is more worrying - comments
• Slop is not necessarily the future - comments
• Oracle slashes 30k jobs - comments
• OpenAI closes funding round at an $852B valuation - comments

If you enjoy such links, I send over 30 every week. You can subscribe here: https://hackernewsai.com/

Yep. Let that sink in for a bit. From 14 to 3... That's 11 people let go from each team.

Source (podcast with Owen Jennings, executive officer and business lead at Block)

Says they "rebuilt" their team around AI agents. Their internal tools take a feature to 85-90% completion on their own. Humans are only required to finish the last 10%.

Would love to know if others are seeing similar things at their companies or if Block is still an outlier.

I’m one of the builders behind Tesana, so this is a self-post.

I’ve been testing to build a small cozy farm sim game: a top-down RPG loop with NPCs, quests, and basic combat. The core idea was to see how far you can get using natural language + iterative edits instead, rather than trying to one-shot a whole game.

Starter prompt:

I started with a high-level prompt describing the world, player controls, and a simple quest chain: I want a cute, top down farm sim where im building a farm, herding animals and growing plants - while trying to stay alive at night from dangerous beasts

Build time:

- Initial playable v1: ~10–15 minutes of prompting
- Adding 3–4 quest steps with conditions: ~30–45 minutes with iteration

Happy to share more details in the comments for anyone curious!

Started like most people do - ESP32-CAM for basic vision tasks. Face detection, simple object detection, cloud inference for anything heavier.

Hit the ceiling fast.

Moved to Jetson Orin Nano 8GB for the main vision compute. The gap is significant enough that it's worth writing up.

What ESP32-CAM handles fine:

• Simple presence detection
• Basic face detection (if you're okay with cloud)
• Streaming video to a host machine

What it can't do:

• On-device inference beyond the most basic models
• Multi-model concurrent inference
• Anything requiring depth or pose estimation
• Real-time tracking without cloud dependency

What Jetson Orin Nano unlocks:

• YOLO11n at 25-30 FPS on-device
• MiDaS depth estimation concurrently
• Full MediaPipe stack (face + hands + pose) in parallel
• TensorRT INT8 optimization: 30-40 FPS full stack
• ROS2 native integration

The ESP32 still lives in my robot stack - handling motor control, sensor reading, low-level I/O. Jetson handles vision exclusively. Clean separation.

If you're building anything that needs real perception and you're hitting ESP32 limits, Orin Nano at $249 is the honest next step. Not a microcontroller anymore but the jump is worth it.

Full vision stack open source: github.com/mandarwagh9/openeyes

What's everyone using for vision on more capable robot builds?

I’ve been building a workflow in ComfyUI that lets me generate consistent character and object animations using LoRAs driven by video motion.

The setup basically takes a video, extracts frames, optionally upscales or removes the background, then runs a dual-stage sampling process with LoRA conditioning to keep the character consistent across frames, and finally reconstructs everything back into an animation.

One of the biggest advantages is that it helps solve the usual inconsistency issues you get with diffusion across sequences, and it works pretty well for both characters and objects or even sprite-style outputs. I’m currently using this for a real project a historical narrative game set during the Hussite Wars mainly to prototype animations quickly and test gameplay systems before committing to final assets as a solo dev.

I’ve shared the full workflow through screenshots, so you can recreate the node setup directly and plug in your own LoRAs or models. If anyone wants help setting it up, improving results, or adapting it for their own use case, feel free to ask questions or DM me, I’m happy to help 👍.