This story was originally published on HackerNoon at: https://hackernoon.com/google-unveils-antigravity-ide-an-ai-driven-coding-environment-powered-by-gemini-3.
Google’s new Antigravity IDE uses Gemini 3 to plan, code, and test applications automatically, offering developers a fast, AI-powered coding experience.
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This story was written by: @proflead. Learn more about this writer by checking @proflead's about page, and for more stories, please visit hackernoon.com.

Google has released a new tool for developers called Google Antigravity IDE. This new software is built around Google’s advanced AI model, Gemini 3. The main goal of this tool is to make coding faster and easier by letting an AI “agent” handle many of the difficult tasks.

This story was originally published on HackerNoon at: https://hackernoon.com/baden-bowers-ai-system-underpins-its-market-leadership-in-pr-delivery.
Baden Bower uses proprietary AI to predict media placements, automate PR workflows, and deliver guaranteed publication outcomes for clients worldwide.
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This story was written by: @jonstojanjournalist. Learn more about this writer by checking @jonstojanjournalist's about page, and for more stories, please visit hackernoon.com.

Baden Bower has built its PR dominance through an AI system that predicts editorial acceptance, automates pitch workflows, and secures guaranteed placements. Serving 3,600 clients, the firm analyzes thousands of editorial patterns, scales operations globally, and delivers features in as little as 72 hours. Its data-driven model reshapes PR by reducing uncertainty and compressing timelines industry-wide.

This story was originally published on HackerNoon at: https://hackernoon.com/why-ai-coding-agents-suck-at-product-integrations-and-how-membrane-fixes-this.
AI coding agents excel at building features but fail at production integrations. The issue isn't AI capability—it's lack of integration-specific infrastructure.
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This story was written by: @membrane. Learn more about this writer by checking @membrane's about page, and for more stories, please visit hackernoon.com.

AI agents can scaffold UIs, call APIs, and generate data models in seconds. But when it comes to building production-grade integrations, they consistently under-deliver. This isn't an AI problem. It's an infrastructure problem.

This story was originally published on HackerNoon at: https://hackernoon.com/can-chatgpt-outperform-the-market-week-17.
New max drawdown...
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This story was written by: @nathanbsmith729. Learn more about this writer by checking @nathanbsmith729's about page, and for more stories, please visit hackernoon.com.

New max drawdown...

This story was originally published on HackerNoon at: https://hackernoon.com/the-next-ai-race-will-start-at-the-application-layer.
AI’s scaling race is ending. With reliable foundation models in place, the real advantage now lies in cost-efficient, fast, application-layer innovation.
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This story was written by: @tyingshoelaces. Learn more about this writer by checking @tyingshoelaces's about page, and for more stories, please visit hackernoon.com.

Engineers have been living a grueling, frustrating slog out of the mud, says Andrew Keen. Keen: The real story isn't a glamorous race to the top; it's been a slog. The new metrics for success are about cost, speed, and creative problem-solving, he says.

This story was originally published on HackerNoon at: https://hackernoon.com/your-ai-has-amnesia-a-new-paradigm-called-nested-learning-could-be-the-cure.
This post breaks down the three most surprising and impactful ideas from this research, explaining how they could give AI the ability to learn continually.
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This story was written by: @hacker-Antho. Learn more about this writer by checking @hacker-Antho's about page, and for more stories, please visit hackernoon.com.

Google Research has introduced "Nested Learning," a new, brain-inspired paradigm that fundamentally rethinks how AI models are built. The paradigm views a single AI model not as one monolithic entity, but as a system of components.

This story was originally published on HackerNoon at: https://hackernoon.com/google-and-yale-turned-biology-into-a-language-heres-why-thats-a-game-changer-for-devs.
The team built a 27B parameter model that didn't just analyze biological data—it made a novel, wet-lab-validated scientific discovery
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This story was written by: @knightbat2040. Learn more about this writer by checking @knightbat2040's about page, and for more stories, please visit hackernoon.com.

A new paper on a 27-billion parameter cell model isn't just about biology. It's data engineering and a blueprint for the future of applied AI. The team built a 27B parameter model that made a scientific discovery.

This story was originally published on HackerNoon at: https://hackernoon.com/how-search-engines-actually-answer-your-questions.
Modern search Q&A explained: how knowledge graphs, DeepQA, and MRC turn messy web pages into direct, trustworthy answers.
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This story was written by: @superorange0707. Learn more about this writer by checking @superorange0707's about page, and for more stories, please visit hackernoon.com.

Modern search Q&A is basically two engines working together: a knowledge-graph engine (KBQA) that’s great at hard facts, and a deep neural engine (DeepQA + MRC) that reads messy web pages like a speed-reading grad student. Around them is a lot of plumbing: data pipelines, retrieval, ranking, fusion, robustness tricks like R-Drop, and even opinion classification on top of long evidence passage