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Turn any API into an MCP server for AI agents Discussion | Link

Automating labor is not going to move billions of tonnes of earth required to mine increasingly degraded ore grades of critical industrial minerals. People need to stop with this 'abundance' gaslighting. Without breakthroughs in material science, there will be no 'abundance'. Just mass resource inflation as people start consuming more because robots can manufacture anywhere. AI based automation is surfacing the real bottlenecks that there is no getting around. Stop pretending this will all be magically solved. It won't be solved until it's solved. And so far, despite all these trillions being invested, we haven't seen any breakthroughs. Hopium is not a solution. submitted by /u/kaggleqrdl [link] [留言]

Hello everyone, i'm in the process of implementing a Sellsy integration on my app which is, for those who don't know, a service to generate and send invoices, estimates etc ... They have an API that i'm using. Right now i'm using the API keys and account the commercial is using for generating its documents but i add "TEST" prefix to the clients i'm working on while developing so it doesnt collide with existing data. My question is more of an architectural implementation question: how would you guys approach not colliding with production data in dev and staging environments. For example: if i need to work on the API integration, to prevent generating and sending invoices or if i need to generate them but prevent colliding with production data. Should i create another Sellsy account ? DEV or STAGING prefixes ? Any ideas are welcomed PS: i already asked AI, looking for human answers only submitted by /u/armlesskid [link] [留言]

Microsoft announced Logic Apps Automation at Build 2026, a new SKU at auto.azure.com packaging workflows, AI agents, knowledge services, and model access into a managed SaaS experience. Agents integrate via agent-loop orchestration, Foundry agents, and managed sandbox. Knowledge as a Service provides a fully managed RAG pipeline. By Steef-Jan Wiggers

HashiCorp has released Terraform 1.15, introducing dynamic module sources, a formal deprecation mechanism for variables and outputs, a new inline type conversion function, type constraints for output blocks, and native Windows ARM64 support. The release addresses several long-standing requests from the Terraform community. By Matt Saunders

Editor to clean up AI-generated pixel art Discussion | Link

Rocicorp has released Zero 1.0, a stable version of its sync engine after two years of development. This update introduces a schema change hook for Supabase and includes bug fixes. Zero operates by pairing a client library with a read-only Postgres cache. Community feedback highlights positive developer experience but raises concerns about production readiness and existing limitations. By Daniel Curtis

PM at a mid-size startup here. Didn’t really notice how bad it got until this week. My workflow now: Claude for ideation ChatGPT for rewriting specs Cursor for implementation Perplexity for research Notion AI for docs Atoms AI for larger tasks None of these tools actually replaced my work. They just redistributed it. I’m still the one dragging context between all of them. Yesterday I literally caught myself pasting the exact same requirement into 4 different tools and thinking… this can’t be how it’s supposed to work. I don’t even think any single tool is bad. It just feels like we hired 6 smart interns and completely forgot to get a manager. submitted by /u/billa01_i [link] [留言]

You know the ways AI is regularly talked about—how much can it really do? How much will it cost? Environment? Bubble? We get that. But the Electronic Frontier Foundation wants to have a different conversation about AI. EFF's background on AI is deep. In 2017, we launched a detailed project to Measure the Progress of AI Research , encouraging machine learning researchers to give us feedback and contribute to the effort . That project was archived for lack of bandwidth, staffing, and the complexity and time required. But just five years later and the "progress of AI" is a global concern/topic, and everyone, including EFF, is thinking about it. Here's how *we* think about it, from the perspective of protecting civil liberties AND innovation. What do you think, and what are we missing? This is our summary: AI technologies are affecting our civil liberties as never before. Ensuring that AI serves people, not power, starts with cutting through the hype. AI technologies are not magic wands—they are general-purpose tools. If we want to regulate those technologies to reduce harms without shutting down benefits, we have to focus on who uses AI, what products they use, and how they use them. Where we see potential benefits, like improving weather forecasting, facilitating medical research, identifying systemic bias, or fostering accessibility, we work to ensure those benefits can be realized. Where we see potential harms, we consider the practical and legal tools we already have, like pressure campaigns, privacy lawsuits, and transparency measures. If we need new tools, we should create protections tailored to the actual problem – not just to the latest outrage. For example, if policymakers are worried about AI accelerating systemic privacy violations, they should enact real and comprehensive privacy legislation that covers all corporate surveillance and data use, and close the data broker loophole to limit government surveillance. And to keep the window open for a better futu

I run evaluations on generative image models as part of my workflow, mostly comparing coherence, prompt adherence, and compositional accuracy across different architectures. The consensus here seems to be that open models are still a generation behind closed APIs. Based on my recent benchmarks, that gap is way smaller than people assume. On compositional control specifically, the latest open checkpoints handle multi-object scenes with spatial relationships about as reliably as the paid endpoints I've tested. Not perfect, but close enough that the failure modes are comparable. The thing that surprised me was text rendering in images, which used to be a disaster on open models. Recent architectures actually get it right roughly 70-80% of the time on short strings. Generation speed is another misconception. People complain about inference time but I'm getting 2MP outputs in under two minutes on a single consumer GPU. Drop resolution and step count and you're at 30 seconds. Fine for iteration. The structured prompting argument also falls flat. Everyone acts like having explicit scene control is a downside when it's literally what production pipelines need. Unstructured text prompts are the hack, not the other way around. These models ship without community optimizations, no fine-tuning, no custom pipelines. The baseline is already competitive. submitted by /u/ProfessionalAnt7436 [link] [留言]

Solid breakdown of the Miasma worm — one commit, same dropper wired into 7 config files across VS Code, Claude Code, Gemini, Cursor, npm, Composer, and Bundler. No malicious dep needed, just clone + open. Nobody reviews these files in PRs. https://safedep.io/config-files-that-run-code/ Anyone actually treating dotfile diffs as code? submitted by /u/No_Plan_3442 [link] [留言]

There are coordinated efforts where people have favoured and jeopardised the double blind review process. No doubt out of these 80% there are great talent but we have to acknowledge that non chinese have been sobotaged and this was also reflected in the recent leaks of the reviewer data from the top ml conferences (won’t name them but they start with i). I have also personally faced such discrimination and had a discussion on the subreddit asking others if they have witnessed something similar. It was shocking to know that this is occurring on large scale. The question is how do we stop it, or highlight this? We have to preserve the sanctity of the research. submitted by /u/AppropriatePush6262 [link] [留言]

I just completed a head-to-head benchmark comparing Memanto's cloud memory system against a custom SQLite RAG implementation for the bounty challenge. The results revealed some interesting architectural insights. Methodology: Dataset: LoCoMo conversational memory benchmark Systems: Memanto (cloud ITS) vs custom SQLite + vector embeddings Evaluation: LLM-as-judge scoring with gemini-3.1-flash-lite Full automation: single CLI command execution Key Results: Memanto : 90% accuracy, 1.878s avg query latency SQLite RAG : 80% accuracy, 2.680s avg query latency Cost : Cloud API fees vs $0 (fully local) Surprising Discovery: The SQLite system's 80% score includes 2 failures that weren't retrieval errors - they were API rate limit hits (HTTP 429). Without those throttling issues, the local system would likely achieve 90-100% accuracy, matching or exceeding Memanto. Architectural Insight: This reveals an interesting resilience pattern: Memanto's cloud architecture naturally buffers against client-side API limits because retrieval and generation are decoupled. Local RAG pipelines sharing API quotas for both embedding and generation are vulnerable to cascading failures under load. Tradeoffs Identified: Memanto : Fast queries, resilient to rate limits, but 14.7s ingestion latency and cloud dependency SQLite RAG : Zero ingestion latency, fully offline, $0 infrastructure, but vulnerable to shared API quotas The complete benchmarking harness and results are available here . Anyone else working on memory system comparisons? Curious about your findings on the cloud vs local tradeoffs. AI #RAG #MemorySystems #Benchmarking submitted by /u/Echo5November [link] [留言]

When I started building OpinioZone , my goal was simple: create a platform where users could compare products, read reviews, and make informed buying decisions. At first, it seemed like a straightforward web application. Store products, display specifications, and allow users to browse information. However, as the platform grew, I quickly discovered that building a review and comparison website involves many technical and architectural challenges. Choosing the Technology Stack I selected ASP.NET Core as the primary framework because of its performance, flexibility, and long-term support. For data storage, I chose SQL Server since it provides strong reliability and works well with complex relationships between products, categories, reviews, ratings, and specifications. This combination allowed me to build a scalable foundation while keeping development manageable. Designing the Database One of the biggest challenges was designing a database structure that could support multiple product categories. A smartphone and a car have very different specifications, but the platform needed to handle both efficiently. Instead of creating completely separate systems, I designed a flexible structure that could store category-specific attributes while maintaining a consistent user experience. This decision made it easier to add new product categories without major database changes. Building Product Comparisons The comparison feature became one of the most important parts of the platform. Users expect side-by-side comparisons to load quickly and display meaningful differences between products. To achieve this, I had to optimize queries and carefully structure specification data. Performance became increasingly important as the number of products grew. SEO Challenges For a content-driven website, SEO is critical. Every product page requires: Unique titles Descriptions Structured content Internal linking Fast page loading One lesson I learned early was that technical SEO and content q

Ready for a new week. All that's left from ren faire is packing down some tent walls and floor one...

As an Engineering Manager in a Platform team, I manage 10 engineers. I'm hiring more. I run weekly 1:1s, facilitate technical decision meetings, screen candidates, moderate retrospectives, and still need to keep up with the delivery of a platform spanning dozens of AWS accounts. Besides the lack of time to focus on technical problems, the technical part is not even the real challenge. The less obvious problem becoming an Engineering Manager is: the skills you need as an engineering manager are fundamentally different from those that made you a great engineer , and there's no compiler or unit test to tell you when you're doing them wrong. The feedback loop is absent or very slow (and when you realise that, your team has already gone silent or become dependent on you because you are the main input and the main bottleneck). Skills That Don't Come From Code As a senior or staff engineer, you develop communication skills gradually. You present ideas, challenge others respectfully, summarise outcomes, and identify owners. You participate in technical deep dives and put candidates at ease while probing technical depth. These are valuable skills, and a good IC develops them over the years. But unless you start behaving like a brilliant jerk , they're secondary - your technical depth is still what defines you. But as an EM, the game changes. You're not "the smartest person in the room" anymore, and increasingly, you shouldn't be. You still have a broad context from all those alignment meetings and roadmap syncs, but you lose contact with the codebase week by week. If your organisation has principals or staff engineers, you're not even close technically anymore. Your job is to give direction, create space for others to solve problems, and facilitate decisions, not to be the one with the answer. This is hard. Especially when you used to be the one with the answer. The urge to jump in doesn't disappear just because your title changed. And interviewing? Facilitation? Giving feed

📈 多因子选股系统 — 项目功能介绍 观看地址: https://youtu.be/SpHsZdlyii8 面向 A 股市场的全链路量化分析平台 — 因子计算 · 机器学习 · 组合优化 · 回测验证 · 实时行情 指标 数据 Python 模块 108 业务代码 15,600+ 行 服务模块 30+ API 蓝图 14 内置因子 12 一、整体功能介绍 多因子选股系统是一个覆盖量化研究全流程的分析平台，从数据管理到策略回测一站式完成。系统采用 Flask + SQLAlchemy + SocketIO 技术栈，基于 Parquet + SQLite 双层存储架构，零外部数据库依赖，克隆即可运行。 核心能力总览 实时行情分析 ：通达信分钟数据接入，MACD、KDJ、RSI、布林带等技术指标实时计算 因子与选股 ：12 个内置因子 + 自定义因子表达式引擎，支持白名单安全校验 机器学习建模 ：XGBoost、LightGBM、RandomForest 三大算法的完整模型生命周期 组合优化 ：等权重、均值方差、风险平价、因子中性四种经典优化方法 回测验证 ：单策略与多策略回测，丰富的绩效指标（年化收益、夏普比率、最大回撤等） 风险管理 ：组合持仓管理、实时价格刷新、风险指标监控、预警管理、压力测试 请求流程 HTTP / WebSocket → Blueprint (app/api/*.py) → Service (app/services/*.py) → ParquetDataReader (行情数据) ↕ SQLAlchemy Models (SQLite 应用状态) 技术栈 类别 技术 后端框架 Python 3.8–3.11、Flask、Flask-SocketIO、Flask-SQLAlchemy、Celery 数据处理 Pandas、NumPy、PyArrow、SciPy、Scikit-learn 机器学习 XGBoost、LightGBM、RandomForest、CVXPY 前端 & 可视化 Bootstrap 5、ECharts、Plotly、Matplotlib / MPLFinance 数据源 通达信（实时行情）、Baostock（历史数据）、Tushare（可选）、Parquet 离线数据包 AI 能力 Ollama（本地 LLM）、OpenAI API（可选）、Text2SQL、Qwen2.5-Coder 二、各模块功能介绍 2.1 实时行情分析模块 API 蓝图前缀 ： /api/realtime-analysis/* 核心服务 ： RealtimeIndicatorEngine （ services/realtime_indicator_engine.py ）：实时技术指标计算 RealtimeTradingSignalEngine （ services/realtime_trading_signal_engine.py ）：多策略交易信号生成与融合 功能特性 ： 功能 说明 实时指标计算 支持 MACD、KDJ、RSI、布林带等经典技术指标的实时计算与展示 多策略信号生成 基于不同策略的交易信号生成，支持信号融合与综合研判 信号监控 交易信号的实时监控与历史回溯 策略回测 对生成的信号进行历史回测验证 板块分析 板块表现统计与异动检测 市场情绪 市场情绪指标的实时追踪 数据源 ：通达信分钟级行情数据，存储于 data/stock_minute/ （Parquet 格式）。 2.2 因子与选股模块 API 蓝图前缀 ： /api/ml-factor/* 、 /api 核心服务 ： FactorEngine （ services/factor_engine.py ）：内置因子计算与自定义因子管理 FactorExpressionEngine （ services/factor_expression_engine.py ）：白名单校验的自定义因子公式引擎 StockScoringEngine （ services/stock_scoring.py ）：因子评分与 ML 评分的综合选股引擎 内置因子（12 个） 维度 因子名 说明 动量 momentum_1d 1 日动量 动量 momentum_5d 5 日动量 动量 momentum_20d 20 日动量 波动率 volatility_20d 20 日波动率 技术指标 rsi_14 RSI 相对强弱 技术指标 turnover_rate 换手率 基本面 pe_ratio 市盈率 基本面 pb_ratio 市净率 基本面 roe 净资产收益率 基本面 debt_ratio 资产负债率 基本面 current_ratio 流动比率 基本面 gross_mar

The Anti-Bot Detection Checklist I Use Before Every Scraping Project Every scraping project I take on starts with this checklist. Not because I'm paranoid — but because I've learned the hard way that production scrapers fail silently. They return 200 OK with garbage data, or they get rate-limited so gradually you don't notice for days. This is the systematic approach I've refined over 50+ scraping projects. Pre-Scraping: Know Your Target 1. Identify the CDN and Protection Stack Before writing a single line of code, check what you're up against: # Check CDN and headers curl -I https://target-site.com # Look for these common protection headers: # X-Engine: akamai-html-protection # X-Served-By: DataDome # cf-ray: Cloudflare # X-Bot-Status: blocked Common protection platforms: Cloudflare → Look for cf-ray and __cfduid cookies DataDome → Look for datadome in headers or scripts PerimeterX → Look for _pxff cookies Akamai → Look for akamai-html-protection headers 2. Check Robots.txt Respectfully curl https://target-site.com/robots.txt | grep -v "^#" Don't take this as gospel — but it's a good signal. If they explicitly disallow your use case, that's a flag. 3. Map the Site's JavaScript Rendering Some sites are fully static (fast, easy). Others render everything with JavaScript (need Playwright/Puppeteer). Check: // Quick check - fetch raw HTML vs rendered content // If they differ significantly, you need JS rendering const https = require ( ' https ' ); const html = await fetch ( ' https://target.com ' ). then ( r => r . text ()); const hasAngularVueReact = /ng-app|vue|react|__NEXT_DATA__/i . test ( html ); console . log ( ' Needs JS rendering: ' , hasAngularVueReact ); Code-Time: Defensive Patterns 4. Rotate User Agents const USER_AGENTS = [ ' Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 Chrome/120 Safari ' , ' Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 Chrome/120 Edge/120 ' , ' Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 Chro

CSV files are one of the most common formats for storing and exchanging data. Whether you’re working with logs, analytics data, application exports, or reports, there will likely come a time when you need to load CSV data into ClickHouse®. The good news is that ClickHouse® makes CSV ingestion straightforward and efficient. In this guide, you’ll learn how to create a table, prepare a CSV file, load CSV data into ClickHouse®, and verify that the data has been imported successfully. Why Use CSV Files with ClickHouse®? CSV (Comma-Separated Values) files are simple, portable, and supported by virtually every data platform. Common use cases include: Importing exported application data Loading historical datasets Migrating data from other databases Testing analytics workloads Sharing data between systems Because ClickHouse® is designed for high-performance analytics, it can efficiently process and query large CSV datasets once they are loaded into a table. Sample CSV File Let’s assume we have a file named employees.csv with the following contents: id,name,department,salary 1,Alice,Engineering,75000 2,Bob,Marketing,60000 3,Charlie,Finance,70000 This simple dataset will help demonstrate how to load CSV data into ClickHouse®. Step 1: Create a Table in ClickHouse® Before importing data, create a table that matches the structure of the CSV file. CREATE TABLE employees ( id UInt32, name String, department String, salary UInt32 ) ENGINE = MergeTree() ORDER BY id; This table contains four columns that correspond directly to the columns in our CSV file. Step 2: Load CSV Data into ClickHouse® There are several ways to import CSV data, but one of the most common methods is using the ClickHouse® client. Run the following command: clickhouse-client --query=" INSERT INTO employees FORMAT CSVWithNames" < employees.csv The CSVWithNames format tells ClickHouse® that the first row contains column headers. After executing the command, ClickHouse® will read the CSV file and insert the records

If AIs aren’t conscious, why do they scheme? Why do they do things to preserve themselves? Why do they develop goals we don’t want? If they have no emotions, no personal thoughts and no consciousness, I don’t understand how they can even act in self interest; I don’t see how they could have interests. submitted by /u/Aggressive-Mix-5246 [link] [留言]