Show HN: AI-first open-source alternative to Microsoft and Google Office https://ift.tt/XiSHo2c

Show HN: AI-first open-source alternative to Microsoft and Google Office InjOffice — AI-first open-source alternative to Microsoft Office and Google Workspace https://ift.tt/sBx9Jbt September 9, 2026 at 02:24AM

Show HN: Self-host open-source LLMs on AWS with scale-to-zero https://ift.tt/aYOeXc8

Show HN: Self-host open-source LLMs on AWS with scale-to-zero https://ift.tt/UsF3gdl September 9, 2026 at 01:07AM

Show HN: Raven – Encrypted messages hidden inside ordinary text https://ift.tt/R806rZX

Show HN: Raven – Encrypted messages hidden inside ordinary text Solo dev. built in my spare time. Raven uses a combination of steganography and cryptography to create a metadata resistant messenger. It encrypts any piece of text on your device and stores the ciphertext on Solana. It works pairwise with keys, users decrypt locally on their device- no accounts. I do run the relayer and a retention sweeper (not claiming peer to peer). One more thing, every write is signed by the same pool wallet, so anyone can see volume/timing. Check the white paper below and there’s been no audit. Several variations include: public, private, and stealth (for fun, i’ve hidden a public raven in this post) White Paper: https://ift.tt/pZsqKJU The crypto and encoding core is open source. Apps are not open yet. Open source: https://ift.tt/wCrjlxN iOS: https://ift.tt/eS7h2v5... Chrome: https://ift.tt/K2Hcpwf... For fun public raven read key: https://sendraven.ink/k/Ot2YzGEoYYaMN1Uc https://sendraven.ink September 9, 2026 at 12:11AM

Show HN: Zero downtime embedding model upgrades https://ift.tt/z5qmyoH

Show HN: Zero downtime embedding model upgrades People use embedding models all the time for rag/semantic retrieval. However, when a newer, more desireable model comes out, there is an expensive (both in time and computational) cost of re-embedding every document in the database. However, I figured out an interesting way to forgo that upfront embedding cost. algo: old model/index -> retrieve top-K docs -> score those docs with the new model -> cache/materialize the new embeddings so instead of rebuilding the entire vector store upfront, the old index keeps getting retrieved from, while the new model reranks those candidates. This works surprisingly well for some model pairs, (i tested 63 source-> target migrations on h100s, on upto 1M documents). For example, on a 1M document Natural Questions dataset, native Qwen3-Embedding-8B: 0.6812 nDCG@10 Qwen3-4B -> Qwen3-8B, K=50: 0.6816 Qwen3-0.6B -> Qwen3-8B, K=50: 0.6638 MiniLM -> Qwen3-8B, K=50: 0.6486 (the hard part is determining k, I held the k constant above to give some sense of migratability). You can install it with pip pip install embedflow and the code is on github https://ift.tt/AecTbXC https://ift.tt/AecTbXC September 7, 2026 at 11:35PM

Show HN: NYC MapTap – Learn NYC neighborhoods (with subway routes when you miss) https://ift.tt/f0gbWHY

Show HN: NYC MapTap – Learn NYC neighborhoods (with subway routes when you miss) https://albertjoseph0.github.io/nyc-maptap/ September 7, 2026 at 11:36PM

Show HN: Server hardening playbook where every item is failure/fix/verify https://ift.tt/aUThb4Y

Show HN: Server hardening playbook where every item is failure/fix/verify https://ift.tt/x8ZIQDt September 7, 2026 at 01:31AM

Show HN: Golden hour API and a 3D globe of the best light https://ift.tt/zPV7T18

Show HN: Golden hour API and a 3D globe of the best light https://ift.tt/Jkln5Dc September 7, 2026 at 01:18AM