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solvX Lab · AI Intelligence

The AI Briefing

The handful of AI stories that actually matter for business — pulled from 70+ sources across the labs, research, and the community, then ranked, de-duplicated, and summarised. The signal, minus the hype.

Updated 13 July 2026

This briefing is built by the public — vote more like this or less on any story, and your choices steer which sources we surface tomorrow.

Off the beaten track

Five the filter held back

More experimental, community, and offbeat picks the strict method normally suppresses. Vote one up if you'd want more like it — that's the signal we use to loosen the filter.

1

Compressed Version of Qwen-3.6-27B coming from PrismML - Khosla-Backed Startup Claims Breakthrough With Largest-Ever AI Model on an iPhone

The startup, PrismML, said it has shrunk down Qwen 3.6 , an open-source large language model developed by Chinese internet giant Alibaba , to run on an iPhone 17 Pro. The model has 27 billion parameters , which are roughly similar to the synapses in a brain and can help determine the complexity of the data a model can…

local-aiopen-modelsoffbeat
4

v0.32.0

New release — 3 changes in this version. Follow the link for the full changelog.

local-aiopen-modelsgithub
5

Osaurus

Open source agents that run 100% locally on your Mac Discussion | Link

productsstartupstools

Shape the briefing

Suggest a source or story

Know a feed we should be reading, or a story we missed? Tell us — it goes straight into the review queue.

How The AI Briefing works

There's far too much AI news to read, and most of it is noise. This page is the opposite of a firehose: an automated pipeline does the reading, scores everything against the same rules every day, and surfaces only the five stories worth your time. No human picks the winners — the method does.

Updated automatically, every morning. A scheduled job runs at 06:30 UTC daily — it re-reads every source, re-scores the day's stories, rebuilds this page, and publishes it. No one presses a button. The "Updated" date at the top tells you the last run.

1

Collect — 70+ sources

It pulls RSS, Atom, and API feeds from the official labs (OpenAI, Anthropic, Google DeepMind), research (arXiv, OpenAlex, Crossref), engineering blogs, and community channels — in one sweep.

2

Score — significance over noise

Each story earns a score weighted toward genuine impact — how many people and organisations it affects, how substantive the change is, whether it moves the field — above mere novelty, plus how much the source is trusted and how fresh it is, minus a penalty for noisy or press-release-style items. The biggest story rises to the top; hype sinks.

3

De-duplicate — no repeats

It remembers what it published on previous days and compares each candidate against that history by keyword overlap. The same story can't appear twice — unless there's a genuine development (a launch, a ruling, a reversal), which it detects and lets back in as an update.

4

Publish — the top five

The five highest-scoring, non-duplicate stories are written straight into this page and a public data feed, then deployed. What you see is the output of the same rules applied to today's news.

Build by public: this isn't just built in the open — it's shaped by the people reading it. Your votes tune which sources rise, your suggestions add new ones, and a daily video recap means you can catch up in two minutes. Built and run by solvX as a working example of the automation we build for businesses. Summaries are drawn from the source publications; follow each link for the full story.

Developers — free JSON API

The daily top 5 is served as JSON at /api/ai-news.json — open (CORS *), GET, cached 30 min, refreshed daily ~06:30 UTC. Reuse with attribution to solvX and the original source.

Response shape

{
  "service": "solvX AI Briefing",
  "date": "2026-06-29",            // YYYY-MM-DD (UTC)
  "generatedAt": "2026-06-29T06:31:00Z",
  "count": 5,
  "stories": [
    {
      "rank": 1,                   // number, 1..5 ascending (rank 1 = top story)
      "title": "string",           // full original headline
      "url": "string",             // canonical http(s) link
      "source": "string",          // e.g. "OpenAI News"
      "category": "string",        // per-story chip, e.g. "SECURITY", "HARDWARE", "BUSINESS"
      "tags": ["string"],          // array of strings
      "summary": "string",         // plain text, 1-3 sentences
      "publishedAt": "string",     // original publish timestamp

      // Video card fields. ON-SCREEN text (read) and VOICEOVER (heard) are
      // DIFFERENT content by design — the voiceover never recites the on-screen lines:
      "headline": "string",        // on-screen card title, <= 40 chars
      "beats": ["string","string"],            // on-screen: EXACTLY 2 facts, each <= 42 chars
      "takeaway": "string",        // on-screen "why it matters", <= 70 chars
      "voiceover": "string",       // SPOKEN narration, 2-3 sentences (~35-55 words), distinct from on-screen

      // v4 — depth + background sources:
      "explainer": "string",       // on-screen CONTEXT the viewer reads, 2-3 sentences (~35-75 words)
      "entities": ["string"],      // canonical company/product names for a REAL logo lookup; [] if none
      "image_keywords": ["string"],// 2-4 concrete visual nouns for a fallback image (no brands/text); [] if none

      // v5 — brand-arrival stinger (used when the story has no real logo):
      "bg_word": "string",         // short subject to show, 1-3 words (~18 chars), Title Case, non-empty
      "bg_tone": "string",         // one of: "launch" | "hot" | "money" | "shock" | "bad" | "neutral"

      // v6 — what/why split for the video:
      "so_what": "string",         // ONE-sentence implication (<=25 words), the "so what" — does NOT restate the event
      "detail": "string"           // one concrete anchor fact (<=24 chars): a number, version, price or proper name
    }
    // ... 5 stories total
  ]
}

Fetch example

const res = await fetch("https://solvx.uk/api/ai-news.json");
const { date, stories } = await res.json();
// stories[] is rank-ordered 1 to 5; map each onto a countdown card with no transform

Field guarantees

On every story: title, url, rank, headline, takeaway, voiceover and explainer are non-empty; beats is an array of EXACTLY 2 non-empty items; entities and image_keywords are always arrays (possibly empty []); all five stories share an identical key set. headline, beats and takeaway are the SHORT on-screen text; explainer is the longer on-screen CONTEXT the viewer reads to understand the story (distinct from the beats); and voiceover is flowing SPOKEN narration written for a different channel — it adds context and does NOT duplicate the on-screen lines. entities holds canonical company/product names for a logo lookup; image_keywords holds concrete visual nouns (never brand names or text) for a fallback background image. bg_word (non-empty, 1-3 words) and bg_tone drive the per-story "brand arrival" stinger shown when there is no real logo — bg_tone is always exactly one of launch, hot, money, shock, bad or neutral. so_what is a non-empty ONE-sentence implication (≤ 25 words) — the "why it matters", written to NOT restate the headline/beats/takeaway/explainer/summary (an overlap check swaps in a consequence line if it would); detail is one concrete anchor fact (≤ 24 chars — a number, version, price or proper name) taken from the story. The card fields and summary are plain text — HTML entities decoded, no markdown, no newlines — and length-capped (truncated at a word boundary, never overflowed) so they drop straight into a fixed-size card.