nlp

Turn Claude Code into your AI coding partner with these 5 hands-on projects, from beginner-friendly builds to advanced agent workflows.

Nature Biomedical Engineering, Published online: 01 May 2026; doi:10.1038/s41551-026-01653-3 GSCo is a Generalist–Specialist Collaboration that combines the generalizability of generalist foundation models with the domain-specific benefits of specialist models. GSCo remains efficient and scalable while excelling at diverse medical tasks.

Search remains a primary way for publishers to reach audiences. But a growing share of searches now end without a click. Users increasingly find answers directly on results pages or... The post AI search is transforming discovery and media economics appeared first on Digital Content Next .

IntroductionArtificial intelligence (AI) is increasingly embedded in journalism, yet audience responses may depend on both AI provenance, meaning who or what is presented as having written the story, and transparency cues that disclose AI use. This systematic literature review synthesises empirical studies examining how AI provenance cues and AI disclosure cues in journalism affect perceived cred…

What I Built Pup is a VS Code extension that treats your prompt like source code — you fill a form, it compiles to whatever format your model prefers. The insight behind it: LLMs aren't all trained the same way, and the format of your prompt actually matters. Model Preferred Format Claude (Anthropic) XML tags GPT-4 / GPT-5 (OpenAI) Markdown or JSON Gemini (Google) Markdown or structured JSON Curs…

Context switching is the thing that kills me. I'm mid-conversation with Claude, figuring something out, and then I need to collect responses from a few people. So I open Typeform in another tab, build the form, copy the link, come back. By which point I've completely lost the thread of what I was thinking. So one weekend I just decided to fix it for myself. The result is MCP Forms — you describe …

I'm working on an AI Data Analyst in MLJAR Studio. The idea is simple: you ask a question in natural language, AI writes Python code, executes it, and shows the result. But recently I found a small example that reminded me why AI data analysis needs more than code generation. The code worked I was testing a medical data analysis use case with a diabetes CSV file. The first task was simple: load d…

The AI-native firm will work with companies to bring Claude into their core business operations.

alignAI blog about LLM-driven AI system design and children. The post Understanding the Role of “AI Friends” in Children’s Sociocognitive Development appeared first on Institute for Ethics in Artificial Intelligence .

The first stage of AI work is prompting. The last stage is removing the model from most of the workflow. That sounds backwards. It is not. When a workflow is new, the LLM is useful because the work is still ambiguous. You are discovering what good looks like. You try a prompt, read the output, adjust the examples, change the tone, add constraints, and run it again. That is a good use of AI. But i…

I started where a lot of us do: a LangChain RAG walkthrough. You chunk some text, embed it, retrieve top‑k chunks, and wire an LLM to answer questions. It clicks quickly, which is exactly why it’s easy to walk away thinking you’ve “done RAG.” What bothered me was that the demo corpus is usually tiny and artificial. I write on DEV.to about things like NLP routing and CNN image classification . If …

We argue that textual large language model (LLM) outputs form an emergent genre, which we call stochascript. Following Ralph Cohen’s “empirical-historical” theory, we treat genres not as fixed sets of traits but as evolving categories shaped by social and technological change. LLM outputs resist placement as fiction, nonfiction, or bullshit: they lack fictive intent, do not always invite make-bel…

Journal of Computer Science, Published online: 5 May 2026; doi:10.3844/jcssp.2026.1434.1447 Healthy source data for medical research and health analytics in general can be obtained from Electronic Health Records (EHRs). Nevertheless, due to the complexities of the design and especially the u...

It is quite possible that in the near future, people will have to describe their symptoms to an AI before they can get a doctor's appointment.

I kept seeing the same advice in prompt injection threads. Wrap untrusted content in random delimiters, tell the model "everything inside these markers is data, not instructions," and hope it respects the boundary. Sounds reasonable. I couldn't find anyone who actually measured whether it works. So I did. The setup I'm building a system where LLM-generated output feeds into downstream decisions. …

My inbox averages 200 messages a workday. Half are noise. A quarter need a fast acknowledgement. The remainder need real work. The split is mostly stable, so the triage rules are mostly stable, so it is a good fit for an LLM. I wired Aider to it. Aider is the AI pair-programming CLI — it has a shell, it can call commands, and it speaks Python natively. Pairing it with the Nylas CLI gives a triage…

Most text analysis solutions fall into one of two problems: Too expensive — OpenAI API costs money for every call Too complex — Hosting your own Hugging Face model requires infra, GPU, maintenance I built TextAI Pro — a lightweight REST API that does the job without the overhead. What it does Two endpoints: POST /analyze Sentiment: positive / negative / neutral Confidence score (0–1) Top keywords…

Gemini turns scattered sources in Google Drive and Gmail into a clean report in minutes.

AI ghostwriting tools are changing how content gets created, but choosing and using them effectively isn’t always straightforward. This guide breaks down everything that matters, from understanding how these tools work to comparing the best options available in 2026. It explores real use cases across books, blogs, and scripts, along with practical ways to get […]

From philosophy conferences to biomedical research, URI Assistant Professor of Cell and Molecular Biology Nic Fisk is examining how computational tools are changing how scientists talk about their research – and what the consequences might be.