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71% of Americans Say AI Could ‘Put People Out of Work Permanently’

Most Americans now see AI as a threat to their livelihoods, with71% fearing it could permanently wipe out jobs. The findings come from a new Reuters/Ipsos poll, which shows widespread anxiety across the US as AI threatens job security and challenges the future of employment. The World Economic Forum.. read more  

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MCP Vulnerabilities Every Developer Should Know

MCP’s blowing up across platforms—but the security? Still sketchy. Think tool description injection. Botched OAuth. Open doors to supply chain attacks. The new MCP 2025-06-18 spec tries to clean house (no token passthrough, mandatory user consent), but most real-world setups either drag their feet .. read more  

MCP Vulnerabilities Every Developer Should Know
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Context Engineering for AI Agents: Lessons from Building Manus

Failures make great teachers—especially for LLMs. Stuffing failed attempts right into the prompt helps agents recalibrate. It nudges their internal priors, cuts down on repeat mistakes, and sparks smarter behavior... read more  

Context Engineering for AI Agents: Lessons from Building Manus
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Building AI Products In The Probabilistic Era

Modern AI broke the rulebook. By spitting outstochastic outputs from unbounded inputs, it flipped software dev from a game of precision to one of probability. Old tools—funnels, SLO dashboards, crisp A/B tests—don’t quite fit anymore. They were built for systems that behaved. Today’s AI stacks mov.. read more  

Building AI Products In The Probabilistic Era
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Is GPT-5 really worse than GPT-4o? Ars puts them to the test.

OpenAI walked back its latest release after users flaggedGPT-5for sounding flat, hallucinating more, and losing creative spark. The fix? Rolling back to the friendlierGPT-4o. Head-to-head tests told a nuanced story:GPT-5nailed accuracy and structure across most prompts. But when the task called for.. read more  

Is GPT-5 really worse than GPT-4o? Ars puts them to the test.
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Tiny Agents in Python: a MCP-powered agent in ~70 lines of code

A new demo walks through buildingTiny Agents in Python—just ~70 lines using theModel Context Protocol (MCP). No boilerplate. Just clean LLM-to-tool hookups with standardized agent configs. Agents plug into multiple MCP servers out of the box—from local filesystems to Playwright browsers—and handle .. read more  

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Myth Or Reality: Will AI Replace Computer Programmers?

Generative AI tools likeGPT-4oandClaude Sonnetnow handle the grunt work—fixing bugs, cranking out code, writing docs—with scary accuracy. Amazon and Anthropic are already hinting at hiring fewer engineers. But the jobs aren’t vanishing; they’re mutating... read more  

Myth Or Reality: Will AI Replace Computer Programmers?
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Building an AI-Powered E-commerce Chat Assistant with MongoDB

freeCodeCamp dropped a new course that walks devs through building an AI-powered shopping agent from scratch. It ties togetherLangGraphfor orchestration,Geminifor reasoning, andMongoDB Atlasas the vector memory layer. The build covers aNode.js backend, aReact frontend, and wires inmulti-step agent .. read more  

Building an AI-Powered E-commerce Chat Assistant with MongoDB
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Evolving our real-time timeseries storage again: Built in Rust for performance at scale

Datadog just dropped its 6th-gen real-time timeseries engine:RTDB. It's built inRust, sharded per core, and backed by LSM trees that don’t blink under pressure. The secret sauce? A custom storage engine calledMonocle—optimized for high-cardinality chaos and bursty workloads. It’s pulling60x faster .. read more  

Evolving our real-time timeseries storage again: Built in Rust for performance at scale
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Worktrees: Git's best kept secret (and why you should use them)

Git worktrees have been around since 2015, but few devs use them like they could. They let you work on multiple branches at once—each in its own directory—without the usual stash-switch-stash-repeat dance. The real power move? Pair them with abare repo. That gives you a clean, central base where ea.. read more  

Worktrees: Git's best kept secret (and why you should use them)
GPT-5.3-Codex is OpenAI’s advanced agentic coding model, designed to go beyond writing code and operate as a general-purpose collaborator on a computer. It builds on GPT-5.2-Codex by combining stronger coding performance with improved reasoning and professional knowledge, while running about 25% faster. The model is optimized for long-running tasks that involve research, tool use, and complex execution, and it performs at the top of industry benchmarks such as SWE-Bench Pro and Terminal-Bench.

Unlike earlier Codex models that focused primarily on code generation and review, GPT-5.3-Codex can reason, plan, and act across the full software lifecycle. It supports activities such as debugging, deploying, monitoring, writing product requirement documents, creating tests, and analyzing metrics. It can also autonomously build and iterate on complex applications and better interpret underspecified prompts, producing more complete and production-ready results by default.

A defining feature of GPT-5.3-Codex is its interactive, agentic workflow. Users can steer the model while it is working, receive progress updates, and adjust direction without losing context, making it feel more like a teammate than a batch automation tool. The model was even used internally to help debug its own training and deployment processes. GPT-5.3-Codex is available through paid ChatGPT plans in the Codex app, CLI, IDE extension, and web, with API access planned for the future.