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The problem with prompt injection testing In 2023, prompt injection was the big concern. "Ignore previous instructions" was the attack. Modern models refuse those prompts well. In 2026, the attack surface changed. The model has tools.
An inference server cut off from the Internet. A dedicated disk for models. Mistral's tokenizer downloaded in advance, sitting in its folder. And at startup: FileNotFoundError: No local files found for the repo ID mistralai/Mistral-7B-v0.1 and revision None. The file is there.
A team compares two versions of an AI service. Average accuracy rises from 82% to 86%. The new version looks better, so releasing it seems like the obvious decision. Then an engineer separates the failures by type.
Can Jev predict Bitcoin? On the test I ran, no. I sent Jev the same 12 technical indicators that a plain gradient-boosting model used, for 1,500 BTCUSDT 5-minute candles neither had seen. Jev answered the control questions correctly 99.8% of the time, so it read the numbers fine.
I run a handful of agents on my laptop. One files notes for me, one answers questions about my projects, one watches news and tells me when something matters. Each of them is fine on its own. Together they are forgetful in a way that took me a while to name.
I participated in a competition and it gave me an insight into something interesting and it led me to build Vicquant. An offline financial advsior app but when i was building it, i ran into a problem and and this aid me in solving real world problem and be innovative for the time being.
I shipped two new endpoints to the x402 catalog today — both $0.0005 per call, USDC on Base, no signup. What they do /api/page-archaeology?url=<URL> — detect parked / thin / auto-generated pages When an agent cites a source, it needs to know if that source is human-curated or a parked domain.
Hello Developers! 👋 I wanted to share Maliklang V1, an open-source, independent programming infrastructure built specifically to run code entirely offline, using Hinglish programming logic.
The rapid pace of the crypto ecosystem makes manual tracking of airdrop opportunities nearly impossible. To stay ahead, developers are increasingly turning to AI-powered monitoring agents.
Building a robust DeFi yield scanner requires moving beyond static APIs to dynamic, intelligence-driven data analysis. By combining Python’s data-handling ecosystem with Large Language Models (LLMs), you can create a tool that not only identifies high-yield liquidity pools but also assesses the…
Threat Modeling the Model Context Protocol: Securing Agentic Tools with mcpscan The Model Context Protocol (MCP) has emerged as an open standard connecting LLM interfaces (such as Claude Desktop and Claude Code) to local and remote execution environments.
프로젝트가 뭔데? 야, ChatGPT가 레고 조립 코드를 만든다고? 원문에 따르면 LDraw라는 레고 조립 전용 언어(.ldr, .mpd)를 GPT‑6 Astra와 Opus 5.5가 자동 생성하도록 만든 파이썬 툴셋을 Docker 이미지로 배포했대. 웹 UI가 포함돼서 OpenAI, Claude, OpenRouter 등 여러 모델을 선택해 LDraw 파일을 만들 수 있다. 결과물은 LDView·LeoCAD·Studio 같은 뷰어에서 바로 열어볼 수 있지. 내 VPS에 올릴 수 있을까?
For weeks I planned around a number I had read somewhere. The number was the cost of generating one image, in whatever internal units the provider billed in. I believed it was around 1363 units per image. On my free tier, that worked out to roughly five to seven images a day.
Developers are skeptical by default, and that is a good thing when choosing a Gen-AI course. Here is a technical checklist to score any online GenAI course before you enrol. The scoring checklist Give each item 1 point. A strong course should score 10 or more out of 12.
In 1978, researchers asked 60 people at Harvard Medical School a single question. A disease affects 1 in 1,000 people. The test for it has a 5% false positive rate. A patient tests positive. What's the chance they actually have it? The most common answer was 95%. The right answer is about 2%.
An AI coding agent can inspect your repository, spin up a new endpoint, modify the database schema, wire the frontend, generate unit tests, handle errors, and produce a migration—all in under ten minutes.
Its test suite passed six out of six. Then I pointed it at thirteen real crash reports from my own server and it got three of eight. I asked Claude to build and ship a commercial product with as little help from me as possible — write the code, write the tests, set up the storefront, publish it.
Your agent just told you it sent the proposal. The tool call came back with an id, the trace shows a clean green step, and the customer never got the email. That gap is small and quiet, and it is the thing underneath a loud week.
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