Show HN: MegaDSP an effects plugin for DAWs with 50 effects https://ift.tt/LETD7mt

Show HN: MegaDSP an effects plugin for DAWs with 50 effects Hey HN, I am creating megaDSP, a huge DAW plugin for all platforms, with the goal of it being a free, open-source, and semi or fully autonomous github project that evolves around community feedback. It currently has more than 50 audio effects that can be combined together in effects chains, and also supports "snapshots" where you can seamlessly switch between different effects chains (for live use, like a fancy guitar pedalboard for example). I would really love if you have a moment to test it out, give thoughts, etc. There are many, many plugins out there in the world, but many of them are either DAW-specific or have annoying copy protections etc. Having one huge plugin with great DSP algorithms seems like it could be a benefit to the music/audio world. Thanks, -Kevin first pre-release: https://ift.tt/Q2KGhE6... July 28, 2026 at 11:08PM

Show HN: Building a new game everyday with AI. Day #106 Zombie Survival Training https://ift.tt/f9m8Pxd

Show HN: Building a new game everyday with AI. Day #106 Zombie Survival Training I'm using AI (mostly Claude) to create/publish a new video game every day This is day 106, and my first stab at the zombie/survival genre. Most of the games I build with just a few prompts, but this one I used almost an entire 5-hour window on the $20/month pro subscription (in about an hour) with Opus 5 to get right with all the weapons and such. Then I go outside and touch grass for the next four hours. Happy to answer any questions, just a little hobby project of mine I'm having lots of fun with :) I'm now well into month 4, and I haven't missed a single day of publishing a new game yet! https://ift.tt/0r3CpXV July 27, 2026 at 09:49PM

Show HN: Watch 14-Byte AI "brains" attempt to solve a 2D maze (Its hard) https://ift.tt/Vo4N5RC

Show HN: Watch 14-Byte AI "brains" attempt to solve a 2D maze (Its hard) Hey HackerNews, I built this project over the last few weeks as a palette cleanser from a failed game launch. I wanted to learn a bit about AI/Neural-Networks and naively thought I could build a tiny maze-solving AI in a weekend with a 100% solve rate. Well - I couldn't, but I got pretty close. 14 Bytes total model size, and a 96.5% solve rate on unseen mazes. Trained across 46 phases experimenting with different ideas to improve the model (better performance, smaller size). Its quite fun to watch the model attempt to solve the maze, when they fail its usually due to getting stuck in a loop. The models have no access to coordinates, map-data, or external memory scratches - they must navigate using only immediate local neighbourhood observations. There is a model dropdown and you can see how the model has progressed over each phase, constantly getting smaller and increasing its solve rate. Total trained models number in the thousands - I just expose the winning models from each phase. Overall a fun experiment, with much implementation help from AI agents to scaffold and implement the code (I'm a lazy software dev). https://con-dog.github.io/MINIMIO-PUBLIC-FRONTEND/ July 27, 2026 at 04:15AM

Show HN: Descript wanted $24/mo, I built an open-source alternative in a weekend https://ift.tt/o8vmLhN

Show HN: Descript wanted $24/mo, I built an open-source alternative in a weekend Descript costs $24/mo, so I built this over a single weekend with Fable! Introducing Rescript: edit videos by simply editing the transcript text. Drop in a video and it is transcribed locally with per-word timestamps and speaker labels. Delete words in the transcript and the corresponding clip is cut from the video. Runs fully in the browser: Local, free, offline and open source. → Github https://ift.tt/FY6WL2I → App https://wassgha.github.io/rescript https://ift.tt/FY6WL2I July 27, 2026 at 03:22AM

Show HN: ASL V6 – Open-source AST red-teaming engine for Python AI agents https://ift.tt/OWVu5Cf

Show HN: ASL V6 – Open-source AST red-teaming engine for Python AI agents https://ift.tt/iL3Tuj0 July 27, 2026 at 12:50AM

Show HN: What 180k words look like as a temporal knowledge graph (Oz series) https://ift.tt/uEeJAPN

Show HN: What 180k words look like as a temporal knowledge graph (Oz series) The graph is free to explore and requires no registration. SynapTale builds a model of a story as a temporal graph made up of nodes (entities) and edges (their actions and relationships). The graph is not a visualization of the wiki. The wiki, timelines, relationship histories, and analytics are projections of the graph. The current demo contains 232 entities, 1,852 edges, and a snapshot of the story’s state at every chapter. By chapter 100, it still remembers a promise made in chapter 8 and turns the story into a set of source-verifiable facts. The most interesting things can be found in the graph itself and in the Analytics tab. A few things I found: 1. The character with the highest kill count is the Tin Woodman—the same character who cries over a beetle he accidentally crushed. Dorothy comes second, with three killing events. 2. Dorothy never deceives anyone during the first 100 chapters of the series. 3. The Scarecrow’s debt to the stork has remained active for 92 chapters, starting in chapter 8. 4. The Cowardly Lion ranks third by number of threats. 5. The first 100 chapters contain 60 secrets and 254 dialogue events. Technical details 1. Five different multi-agent pipelines combining LLMs and NLP: a prescan, ontology construction, chapter-by-chapter graph extraction, retrospective validation over spans of dozens of chapters, and a linguistic prescan for speech profiles and linguistic edges. 2. A living story needs a living graph. It has to account for time, because entities and the relationships between them evolve. A simple is_active field is not enough. I ended up with three types of edges: event: an instantaneous action; identity: a fact; state: a persistent action whose termination requires justification and a supporting quote from the text. The vast majority of edges are events and end in the same chapter in which they began. This allows the system to scale well, since only a minority of state and identity edges remain continuously active. 3. Ontology. You cannot simply ask an LLM to extract entities and relationships into a graph. With every chapter, even the smartest model will keep inventing unimportant fields, creating new aliases for existing fields, and representing the same fields inconsistently. Before extracting the graph, the system therefore performs an ontology scan across the entire story. It captures story-specific entity and edge types, along with their fields and descriptions. 4. Epistemics. Events are only one part of a story. It is also important to understand how information is distributed, which is difficult to represent using event edges alone. I addressed this by introducing a new node type: epistemic nodes, which capture different entities’ perspectives on the same fact. Subtle hints can still be missed, the system is not yet perfect in this area. https://ift.tt/1EM49dm July 25, 2026 at 11:18PM

Show HN: Nostalgia – A shareable Windows 98 desktop builder https://ift.tt/kyVdUiG

Show HN: Nostalgia – A shareable Windows 98 desktop builder https://ift.tt/YMdHuv7 July 25, 2026 at 05:57AM