Show HN: Integer-only AI complete XOR with 99.7% accuracy all from scratch
##ABSL - Adaptive bitshft learning ( v.1.0.0 XOR) ABSL is an experimental, 100% integer-only learning algorithm for neural networks, written from scratch in Rust. By avoiding floating-point math entirely, ABSL doesn't need an FPU. That makes it interesting for low-power embedded systems, 8-bit/16-bit microcontrollers, and neuromorphic hardware.
- ▪##ABSL - Adaptive bitshft learning ( v.1.0.0 XOR) ABSL is an experimental, 100% integer-only learning algorithm for neural networks, written from scratch in Rust.
- ▪By avoiding floating-point math entirely, ABSL doesn't need an FPU.
- ▪That makes it interesting for low-power embedded systems, 8-bit/16-bit microcontrollers, and neuromorphic hardware.
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Record
| Original publisher | GitHub |
| Canonical URL | https://github.com/Mojo0869/ABSL |
| Publication time | Fri, 31 Jul 2026 22:51:20 +0000 |
| Retrieval time | 2026-07-31T23:08:33.083Z |
| Last seen | 2026-07-31T23:08:33.083Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 76_1LHAYIjK0 · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
##ABSL - Adaptive bitshft learning ( v.1.0.0 XOR) ABSL is an experimental, 100% integer-only learning algorithm for neural networks, written from scratch in Rust. By avoiding floating-point math entirely, ABSL doesn't need an FPU. That makes it interesting for low-power embedded systems, 8-bit/16-bit microcontrollers, and neuromorphic hardware. Instead of a fixed learning rate, ABSL scales weight updates using an adaptive bit-shift based on the integer error magnitude. Solving XOR without floats XOR is normally solved with continuous gradients. I wanted to see if a purely integer-based approach could get there too.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at GitHub.