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I built an MCP server that gives AI persistent memory of your SQL database

A while ago I tried to build a local coding assistant. I downloaded Qwen3, fired it up on my MacBook with 16GB of RAM, and within a day realized the output quality was nowhere close to Claude or GPT-5. The model could fit . It just couldn't compete . So I changed the question. If I can't make the model smarter on my hardware, can I make what I feed it smarter? Where the tokens actually go I started watching where my Claude / Cursor / Copilot sessions actually spent their tokens. The surprise: most of it wasn't reasoning. It was lookup . Every fresh chat about my company's database re-discovered the same things: What does status = 3 mean? (cancelled) How does orders join to users ? ( orders.user_id → users.id ) What's that cryptic JobStatus enum? (a dozen integer codes nobody remembers) The model figured it out, the session ended, and tomorrow it figured it out again . Same tokens, same latency, every single time. The expensive part of working with an AI wasn't the thinking — it was re-teaching it things it had already learned yesterday. There's a lot of attention right now on trimming AI output tokens (talk like a caveman, strip the pleasantries, etc.). But in my workflow the bigger leak was on the input side: paying full token cost every session to re-establish context that never changed. "Memory" isn't a feature, it's an architecture question AI clients are starting to bolt on "memory" features. But they're proprietary, opaque, and locked to one tool. Claude's memory doesn't help Cursor. Cursor's doesn't help Copilot. You can't inspect it, you can't share it with a teammate, and you can't diff it. What I actually wanted was an explicit, inspectable, shareable context layer that any AI client could read deterministically — same answer every time, same file my team could hand off. I picked the highest re-learn cost in my world to start with: SQL databases. Enter amnesic amnesic is an open-source MCP server that gives any AI client persistent semantic memory of your

2026-05-28 原文 →
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🔥 mukul975 / Anthropic-Cybersecurity-Skills - 754 structured cybersecurity skills for AI agents · Mapped t

GitHub热门项目 | 754 structured cybersecurity skills for AI agents · Mapped to 5 frameworks: MITRE ATT&CK, NIST CSF 2.0, MITRE ATLAS, D3FEND & NIST AI RMF · agentskills.io standard · Works with Claude Code, GitHub Copilot, Codex CLI, Cursor, Gemini CLI & 20+ platforms · 26 security domains · Apache 2.0 | Stars: 10,997 | 886 stars today | 语言: Python

2026-05-28 原文 →
AI 资讯

Sniffing Modbus Traffic with 5 Lines of Python (And Why It Should Scare Your OT Team)

⚠️ For defensive/educational purposes only. Sniff only networks you own or are explicitly authorized to test. Unauthorized network monitoring is illegal in most jurisdictions. The uncomfortable truth about your factory floor If your plant uses Modbus TCP — and statistically, it probably does — every register read, every coil write, every sensor value is flying across your network in plaintext . No encryption. No authentication. No signature. Nothing. Modbus was designed in 1979 by Modicon for serial communication between a PLC and a few field devices on a dedicated cable. The threat model was "someone might physically tap the wire." The solution was "don't let strangers into the control room." Forty-five years later, that same protocol is running over your corporate VLAN, talking to cloud historians, and occasionally — if your IT/OT segmentation has gaps — reachable from the internet. Let me show you what that looks like from the wire. The 5-line sniffer This is a defensive monitoring tool. Same code your blue team would use to baseline normal traffic and detect anomalies. Requires scapy : pip install scapy from scapy.all import sniff , TCP , Raw def show_modbus ( pkt ): if TCP in pkt and pkt [ TCP ]. dport == 502 and Raw in pkt : payload = pkt [ Raw ]. load print ( f " { pkt [ ' IP ' ]. src } → { pkt [ ' IP ' ]. dst } : { payload . hex () } " ) sniff ( filter = " tcp port 502 " , prn = show_modbus , store = False ) Run it on a span port, a TAP, or a mirror VLAN, and within seconds you'll see something like this: 192.168.1.50 → 192.168.1.10: 0001000000060103006400 02 192.168.1.10 → 192.168.1.50: 00010000000701030441f00000 192.168.1.50 → 192.168.1.10: 00020000000601100065000102 Every byte tells a story. Let's decode the first packet. Decoding what you just captured The Modbus TCP frame format is documented in the spec (it's public — that's part of the problem): Bytes 0-1: Transaction ID Bytes 2-3: Protocol ID (always 0x0000 for Modbus) Bytes 4-5: Length Byte 6: Unit

2026-05-28 原文 →
AI 资讯

I Built an Open-Source Multi-Agent Fact-Checker — Here's How It Works

Problem Statement We have a misinformation problem. But more specifically, we have a speed problem. A journalist spots a suspicious claim. They search for sources. Cross-reference databases. Call experts. Write a verdict. Get it edited. Publish, maybe 6 hours later. Maybe 3 days later. Meanwhile, the original claim has been screenshot, reposted, quoted in newsletters, and cited in arguments across five platforms. I wanted to build something that closed that gap. Not a chatbot that guesses. A proper pipeline, one that retrieves real evidence, reasons from it, and tells you why it reached a verdict. That's what Sift is. What is Sift? Sift (Source Inspection & Fact-checking Tool) is an open-source multi-agent AI pipeline that takes any text, extracts every factual claim, retrieves grounded evidence, and returns auditable verdicts — TRUE, FALSE, or UNCERTAIN, with cited sources and full reasoning chains. Paste a news article. A politician's speech. A viral statistic. A WhatsApp forward. Sift breaks it into individual claims and fact-checks each one independently. Why Multi-Agent? The naive approach is to ask an LLM: "Is this claim true?" The problem: LLMs hallucinate. They have knowledge cutoffs. They're confidently wrong in ways that are hard to detect. And critically, they don't show their work. A single LLM call can't reliably handle the full pipeline of: Extracting structured claims from noisy text Retrieving dated, traceable evidence from live sources Reasoning across conflicting evidence without confabulating Adversarially reviewing its own conclusions for overconfidence Finding corrections when something is wrong Each of these is a distinct task that benefits from its own prompt, its own tools, and its own failure modes. That's why I built five separate agents, orchestrated with LangGraph. The 5-Agent Pipeline Agent 1 — Claim Extractor A single paragraph can contain 4-5 distinct factual claims. Generic LLMs miss them or conflate them. This agent uses LLaMA 3.3 70

2026-05-28 原文 →
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