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ClickFix Attack Uses Windows Terminal to Evade Detection
Fake CAPTCHA pages instruct victims to paste malicious commands in the Windows Terminal instead of the Run dialog.
The post ClickFix Attack Uses Windows Terminal to Evade Detection appeared first on SecurityWeek.
Show HN: FlowEasy – AI generates production-grade CI/CD pipelines in 3 clicks
Article URL: https://floweasy.dev
Comments URL: https://news.ycombinator.com/item?id=47308148
Points: 1
# Comments: 0
Pandas' Public API Is Now Type-Complete
Article URL: https://pyrefly.org/blog/pandas-type-completeness/
Comments URL: https://news.ycombinator.com/item?id=47308131
Points: 1
# Comments: 0
Show HN: Dockportless – No port conflicts running multiple AI agents in parallel
Article URL: https://github.com/mazrean/dockportless
Comments URL: https://news.ycombinator.com/item?id=47308129
Points: 1
# Comments: 0
Teenagers are getting far less sleep now than they did in late 2000s, new study
Article URL: https://medicalxpress.com/news/2026-03-teenagers-late-2000s.html
Comments URL: https://news.ycombinator.com/item?id=47308109
Points: 1
# Comments: 0
Show HN: Local memory layer for AI agents, survives restarts, no embeddings
I got frustrated that every time an AI agent restarts it forgets everything. So I spent six months building a fix.
Synrix is a local-first memory engine for AI agents. It uses a Binary Lattice structure instead of vectors — fixed-size nodes with prefix-semantic addressing. Lookups are O(k) where k is the number of results, not the size of the dataset. The demo that convinced me this was worth sharing: I told GPT-4 my name, that I like pugs and Ferraris, and a few facts about my project. Restarted the session completely.
The side without Synrix forgot everything. The side with Synrix recalled every single detail instantly. No retraining. No embeddings. No API call. Just prefix lookup in microseconds. Real numbers from my machine. Direct node lookup 19μs. Prefix queries over 10k nodes 28-80μs. Full agent context restored from cold start under 1ms. WAL recovery tested across 60 crash scenarios with zero data loss. Validated on Jetson Orin Nano at 192ns hot reads.
It runs entirely in-process. No server, no network, no GPU. Works on a factory floor, underground, on a robot that just lost power. Honest positioning: this is not a vector database replacement. For fuzzy similarity search over unstructured documents Qdrant and Chroma are the right tools. Synrix is specifically for structured agent memory where you control the naming — user preferences, learned facts, session context, task state. You know what you're looking for. Curious whether anyone has hit this problem in production and how you're currently solving it.
github.com/RYJOX-Technologies/Synrix-Memory-Engine
Comments URL: https://news.ycombinator.com/item?id=47308108
Points: 1
# Comments: 0
Cargo thieves are stealing millions of dollars in tech hardware
Article URL: https://www.washingtonpost.com/business/2026/03/09/cargo-theft-ai-chips-data-centers/
Comments URL: https://news.ycombinator.com/item?id=47308106
Points: 1
# Comments: 0
First Look at Firefox Nova
Article URL: https://www.soeren-hentzschel.at/firefox/exklusiv-so-sieht-das-neue-nova-design-von-firefox-aus/
Comments URL: https://news.ycombinator.com/item?id=47308094
Points: 1
# Comments: 0
Since 1960, the world has lost languages – and gained thousands
Article URL: https://asteriskmag.com/issues/13/language-birth
Comments URL: https://news.ycombinator.com/item?id=47308087
Points: 1
# Comments: 0
Show HN: Forge, the NoSQL to SQL Compiler
https://forge.foxtrotcommunications.net/
I've been a data engineer for years and one thing drove me crazy: every time we integrated a new API, someone had to manually write SQL to flatten the JSON into tables. LATERAL FLATTEN for Snowflake, UNNEST for BigQuery, EXPLODE for Databricks — same logic, different syntax, written from scratch every time.
Forge takes an OpenAPI spec (or any JSON schema) and automatically:
1. Discovers all fields across all nesting levels 2. Generates dbt models that flatten nested JSON into a star schema 3. Compiles for BigQuery, Snowflake, Databricks, AND Redshift from the same metadata 4. Runs incrementally — new fields get added via schema evolution, no rebuilds
The key insight is that JSON-to-table is a compilation problem, not a query problem. If you know the schema, you can generate all the SQL mechanically. Forge is essentially a compiler: schema in, warehouse- specific SQL out.
How it works under the hood:
- An introspection phase scans actual data rows and collects the union of ALL keys (not just one sample record), so sparse/optional fields are always discovered - Each array-of-objects becomes its own child table with a hierarchical index (idx) linking back to the parent — no manual join keys needed - Warehouse adapters translate universal metadata into dialect-specific SQL: BigQuery: UNNEST(JSON_EXTRACT_ARRAY(...)) Snowflake: LATERAL FLATTEN(input => PARSE_JSON(...)) Databricks: LATERAL VIEW EXPLODE(from_json(...)) Redshift: JSON_PARSE + manual extraction - dbt handles incremental loads with on_schema_change='append_new_columns'
The full pipeline: Bellows (synthetic data generation from OpenAPI specs) → BigQuery staging → Forge (model generation + dbt run) → queryable tables + dbt docs. There's also Merlin (AI-powered field enrichment via Gemini) that auto-generates realistic data generators for each field.
I built this because I watched teams spend weeks writing one-off FLATTEN queries that broke the moment an API added a field. Every Snowflake blog post shows you how to parse 3 fields from a known schema — none of them handle schema evolution, arbitrary nesting depth, or cross-warehouse portability.
Try it: https://forge.foxtrotcommunications.net
Happy to answer questions about the architecture, the cross-warehouse compilation approach, or the AI enrichment layer.
Comments URL: https://news.ycombinator.com/item?id=47308072
Points: 2
# Comments: 0
No leap second will be introduced at the end of June 2026
Article URL: https://lists.iana.org/hyperkitty/list/tz@iana.org/thread/P6D36VZSZBUSSTSMZKFXKF4T4IXWN23P/
Comments URL: https://news.ycombinator.com/item?id=47308059
Points: 4
# Comments: 0
Show HN: Kairos, real-time AI who cross-verifies (Python, 100KB)
Hi HN, I'm Joshua, a teen from Kerala, India.
I built Kairos because I was frustrated that every AI I tested hallucinated on live events. During today's T20 World Cup Final, ChatGPT named the wrong player. Copilot named a different wrong player. Both confidently. Neither had live data.
Kairos solves this differently. Instead of sending raw search results to the LLM and hoping, it runs a verification pass first — cross-checking titles across RSS, DuckDuckGo, and NewsAPI, scoring each result by how many independent sources confirm it, then sorting by confidence before the LLM ever sees the context.
The architecture:
1. Pronoun resolution from ChromaDB conversation history (no API call) 2. Domain classification (6 domains) 3. Query expansion — 1 query becomes 4 targeted searches, pure Python, zero extra API calls 4. Parallel async fetch with timeouts 5. Cross-verification scoring 6. Dynamic thinking budget (0 for sports scores, up to 10k for complex analysis, hard capped) 7. 250 word output limit
Total codebase: ~90KB Model: Gemini 2.5 Flash Cache: ChromaDB Cost: $0
Benchmark on today's T20 Final: Kairos 43/50, Gemini 40/50, Perplexity 38/50, Copilot 26/50, ChatGPT 19/50
ChatGPT and Copilot both hallucinated player names with full confidence. Kairos cited 15 live sources.
GitHub: https://github.com/joshuaveliyath/kairos
Happy to answer technical questions.
Comments URL: https://news.ycombinator.com/item?id=47308049
Points: 1
# Comments: 0
Ageless Linux – Software for Humans of Indeterminate Age
Article URL: https://agelesslinux.org/
Comments URL: https://news.ycombinator.com/item?id=47308039
Points: 1
# Comments: 0
K-Shaped AI Adoption
Article URL: https://www.jeremyg.dev/k-shaped-ai-adoption/
Comments URL: https://news.ycombinator.com/item?id=47308037
Points: 1
# Comments: 0
Show HN: I built a clipboard mgr with crypto address swap attack detection
Yankput is a Mac clipboard manager built for developers. The core idea: your clipboard is a security hole and nobody is watching it. The feature that started it all — crypto address swap protection. Malware silently replaces crypto addresses in your clipboard while you're pasting. Yankput detects when a copied address changes before you paste and fires a system alert. It also detects and masks secrets (API keys, GitHub tokens, AWS keys, etc.) and auto-deletes them after 5 minutes. Plus an Incognito mode that pauses recording entirely. On top of that: JSON/XML formatter, Base64 encoder, SHA hash generator, regex tester, and line diff — all operating on whatever's in your clipboard. $14.99 one-time. yankput.app Would love feedback from the HN crowd, especially on the security angle.
Comments URL: https://news.ycombinator.com/item?id=47308032
Points: 1
# Comments: 0
Azure AI Search vs. Manticore Search
Article URL: https://manticoresearch.com/blog/azure-ai-search-vs-manticore/
Comments URL: https://news.ycombinator.com/item?id=47308027
Points: 1
# Comments: 0
Notes on writing a voxel game in Dyalog APL
Article URL: https://homewithinnowhere.com/blog/voxel_game/
Comments URL: https://news.ycombinator.com/item?id=47308001
Points: 1
# Comments: 0
Show HN: Bother – model your project as it exists in reality
Article URL: https://bother.now/orbit?demo=true
Comments URL: https://news.ycombinator.com/item?id=47307998
Points: 1
# Comments: 0
A CEO's Guide on Data Readiness for AI on Scaling AI Initiatives
Article URL: https://www.kellton.com/kellton-tech-blog/ceo-guide-data-readiness-ai-scaling-initiatives
Comments URL: https://news.ycombinator.com/item?id=47307993
Points: 2
# Comments: 1
Internet Infrastructure TLD .arpa Abused in Phishing Attacks
Abusing DNS record management controls, the threat actor hides the location of malicious content via Cloudflare.
The post Internet Infrastructure TLD .arpa Abused in Phishing Attacks appeared first on SecurityWeek.
