nowfound

AI · May 13, 2026

ScholaRef

AI Workspace for Academic Submissions

What it does

Turn your manuscript into a submission-ready paper with ScholaRef. From deep manuscript review and AI-detection checks to citation verification, novelty analysis, journal recommendations, reviewer simulation, grammar refinement, and exportable feedback reports, ScholaRef brings the entire academic submission workflow into one powerful AI workspace. ScholaRef helps you catch weaknesses before reviewers do, strengthen your paper’s positioning, and move toward submission with confidence.

Does the same job

all alternatives →
  • Paperguide2024 · paperguide.ai · ▲645

    Discover, read, write and manage research with ease using AI

  • SciSpace AI Academic Writer2024 · scispace.com · ▲682

    Write like a scientist

  • Paperguide AI Writer2024 · ▲444

    Easily write well researched articles & academic papers

  • aiPDF - talk to books, docs and podcasts2023 · ▲381

    ethical, powerful AI assistant for students and researchers

  • ChirpzOct 2025 · ▲297

    Uncover unknown citations with AI

  • AP
    AI Peer Reviewer – Multiagent system for scientific manuscript analysis2025 · github.com · ▲108

    After waiting 8 months for a journal response or two months for co-author feedback that consisted of "looks good" and a single comma change, we built an AI-powered peer review system that helps researchers improve their manuscripts rapidly before submission. The system uses multiple specialized agents to analyze different aspects of scientific papers, from methodology to writing quality. Key features: 24 specialized agents analyzing sections, scientific rigor, and writing quality // Detailed feedback with actionable recommendations. // PDF report generation. //…

More ai this month

the category →
  • I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.

    AI · 17d ago · simedw.com

  • Astute585

    Automate your B2B brand going viral, with new media creators

    AI · 18d ago · company-app.joinastute.com

  • Grok Bot547

    AI teammates that you can give real work to

    AI · 25d ago · x.ai

  • Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…

    AI · 27d ago · cactuscompute.com

  • Monid474

    OpenRouter for agent tools

    AI · 6d ago · monid.ai

  • Turn website visitors into qualified pipeline

    AI · 19d ago · clarasdr.ai

Launched alongside, May 2026

the whole month →
  • Brew 905

    Like Claude design for email marketing

    AI · May 2026 · brew.new

  • Parallel agents, diff reviewer, and multi-model comparisons

    Dev tools · May 2026 · kilo.ai

  • StoreClaw805

    Grow your store profits with agents that know how to sell

    AI · May 2026 · storeclaw.ai

  • Give your agent a real number and voice to make calls.

    AI · May 2026 · pollyreach.ai

  • NW

    Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…

    Life & fun · May 2026 · github.com

  • FM

    Dev tools · May 2026 · github.com