Most people use Claude like a chatbot.

Ask a question. Get an answer. Maybe generate some code or summarize a PDF.

That’s it.

But after spending more time with Claude, I realized most users are barely scratching the surface of what it can actually do.

Claude has quietly evolved into something much bigger than an AI assistant. Hidden behind the normal chat interface are features that can completely change how you work, research, write, code, and organize information.

What surprised me the most is how few people talk about these features.

Here are some of the hidden capabilities of Claude that genuinely changed the way I use AI.

One of the biggest hidden strengths of Claude is its massive context window.

Most AI tools struggle when conversations become too long or when you upload large amounts of information. Claude feels different. You can upload long research papers, entire codebases, huge PDFs, meeting transcripts, or documentation, and Claude can still maintain context surprisingly well.

This becomes incredibly useful for developers and researchers.

Instead of pasting small snippets of code, you can upload multiple files and ask Claude to explain the architecture of an entire project, identify bottlenecks, or suggest improvements. It feels less like autocomplete and more like collaborating with someone who actually understands the bigger picture.

The same thing applies to writing and research. Claude can compare ideas across large documents, summarize complex information, and help organize thoughts without constantly losing track of the conversation.

Once you experience this workflow, normal AI chats start feeling limited.

Another hidden feature that completely changes the experience is Artifacts.

Most people think AI outputs are supposed to be plain text.

Claude does something different.

Instead of simply generating code inside the chat, Claude can create interactive outputs like dashboards, mini web apps, UI layouts, diagrams, and editable documents. The first time I used Artifacts, it honestly felt like the line between AI chat and development environment started disappearing.

You can describe a landing page idea, and Claude generates a working interface. You can ask for a visualization, and it creates something interactive instead of dumping raw code into the conversation.

For frontend developers, designers, and creators, this is one of the most underrated AI features available right now.

Then there are Projects, which most casual users never even touch.

Projects basically turn Claude into a long-term workspace instead of a temporary conversation.

You can organize chats, upload files, add custom instructions, and maintain context around a specific goal or workflow. This becomes extremely powerful when working on something ongoing like a startup idea, a research topic, a coding project, or content creation.

Instead of re-explaining everything every time you open a new chat, Claude already understands the context of the project.

It sounds simple, but the productivity difference is huge.

The AI starts feeling less like a tool and more like an actual collaborator that understands what you’re trying to accomplish.

One of the most powerful but least understood parts of Claude is MCP, or Model Context Protocol.

A lot of people have never heard of it, but developers are starting to realize how important it is.

MCP allows Claude to connect with external tools, APIs, databases, local systems, and development environments. The easiest way to think about it is this:

Most AI systems can only talk.

MCP gives Claude the ability to interact with systems.

That changes everything.

Instead of just discussing workflows, Claude can become part of the workflow itself. It can retrieve information, work with connected tools, analyze external systems, and help automate complex tasks.

This is where AI starts moving beyond “assistant” territory and begins feeling more like an intelligent operating layer.

Another underrated capability is Connectors.

Most people still manually copy-paste information into AI chats. Claude can connect directly with platforms like GitHub, Google Drive, Slack, and other knowledge systems.

That means Claude can reason across your connected information instead of forcing you to constantly feed it context manually.

For example, imagine asking Claude to review documentation across multiple files, summarize GitHub issues, or identify inconsistencies in project notes.

That’s a very different experience from simply chatting with AI.

It becomes a true knowledge assistant.

Something else I noticed while using Claude is how natural its writing feels.

A lot of AI-generated content still sounds robotic or overly polished in a weird way. Claude tends to produce writing that flows more naturally, especially in long-form content.

It’s surprisingly good at:

• restructuring articles
• improving clarity
• maintaining tone
• brainstorming ideas
• editing drafts
• turning rough thoughts into structured writing

For writers and creators, this becomes incredibly useful because the interaction feels collaborative instead of mechanical.

You’re not just generating content.

You’re refining ideas in real time.

Claude is also exceptionally good at reasoning through complicated topics.

Instead of only giving fast answers, it performs well in deeper discussions involving:

• architecture decisions
• tradeoffs
• planning
• systems thinking
• technical explanations
• research analysis

One thing I’ve noticed is that Claude works best when treated like a thinking partner instead of a search engine.

The quality of the interaction changes dramatically when you ask it to:

• compare ideas
• challenge assumptions
• explain reasoning
• evaluate tradeoffs
• simulate discussions

That’s when Claude starts showing its real strength.

The biggest realization for me was this:

Most people still think AI tools are chatbots.

But Claude increasingly feels like something else entirely.

It feels like:

• a workspace
• a research assistant
• a coding partner
• a writing collaborator
• a reasoning engine
• a productivity system

The hidden power of Claude isn’t one feature.

It’s the combination of all these capabilities working together:

• large-context understanding
• interactive artifacts
• persistent projects
• connectors
• MCP integrations
• deep reasoning
• natural writing

Once you start using Claude this way, it stops feeling like a simple AI tool.

It starts feeling like a new way to work with information itself.

This is a submission for the Gemma 4 Challenge: Build with Gemma 4

What I Built

Everbench

Everbench is a low-cost, efficient document research platform for those concerned about privacy.

I’ve been working on a project I called Everknown. It would be an Open Source Evernote replacement. Lately, I’ve stalled on that, having discovered a commercial service that had most of what I wanted, but I have the bones of the app developed and, when I saw this challenge, I decided to put together a small version of Everknown for link/bookmark management and page summarization functions, two of the things that Everknown was going to do for me.

Thus, the odd name Everbench. It’s a “workbench” for Everknown. It has a very simple architecture, but that’s good! Small, composable software that can be modified easily to fit needs.

Everbench conveniently captures web pages, efficiently converts them to Markdown for storage in an Obsidian Vault, creating a summary and tags for categorization. It uses an efficient HTML->MD conversion written in C with a Gemma 4 quality gate to check if the conversion was successful. Some pages can’t be converted (paywalls, login walls, empty SPA shells, mostly-navigation pages, etc.) but Gemma 4 can quickly determine that and characterize the failures. I’ve found that if the conversion fails, the page has serious problems.

Using a deterministic C parser isn’t just about extraction quality; it’s also a small security boundary. The parser strips <script>, <style>,
<noscript>, and CSS-hidden content before anything reaches Gemma 4, so the model never sees what the page is hiding from the user. Feeding raw HTML to an LLM is an open invitation for prompt injection via hidden divs, alt text, JavaScript-emitted content, or whatever the next clever trick happens to be. Prompt injection can be a significant challenge, but we have a place here in processing to insert heuristics to actively guard against it. Gumbo lets me reason about what crosses that boundary.

Demo

<!– Embed a video walkthrough or share a link to your deployed project. –> Here’s a link to the video walkthrough

Code

Everbench

How I Used Gemma 4

Gemma 4 is used for document summarization and categorization, but the novel use of it is as a quality gate for the output from the Gumbo C HTML parser.

The prompt given to Gemma 4 is currently:

You are evaluating whether a web page was successfully extracted into readable Markdown.

URL: <captured url>
Title: <captured title>

Extracted Markdown (first 2000 chars):
<extracted markdown>

Decide if this extraction is GOOD or BAD.

GOOD means: the main article content is present and readable.
BAD means: the content is mostly navigation, advertising, login walls, JavaScript placeholders, or otherwise unusable as a reference.

Respond in exactly this format:
VERDICT: GOOD|BAD
REASON: <one short sentence>

The model is not the processing pipeline; it is the judge inside the pipeline.

In Everknown, I had intended to use local and cloud models for LLM work, interchangeably, configured where it made sense. In Everbench, I just needed an LLM that could categorize (via tags) and summarize documents well. I found Gemma-4-26B-E4B to be excellent at that. The smaller models didn’t do a very good job at some of the things I needed an LLM for, and 31B was too slow and not notably better.

Locally, I can only run one model at a time and I’m hoping that Gemma-4-26B-E4B with its MoE architecture will work out as a good general-purpose local model that I might be able to get some agentic tool-using work out of as I expand projects.

I’m an SRE at Sony Interactive Entertainment. After a week where my teammate had four incidents (and four RCAs), I built something for the blank-page problem after every outage.

What RCAi does

RCAi turns an incident timeline into a structured post-mortem / RCA:

• Executive summary, timeline, root cause, action items
• Usually under a minute to a first draft
• You edit before you ship it anywhere

Free: 3 RCAs lifetime, no credit card.

Templates & export

• Standard, executive, and deep technical templates (higher tiers unlock more)
• PDF / Markdown on paid plans
• Confluence / Notion = copy formatted text to clipboard (not OAuth publish)

Integrations (optional)

Paste credentials per import — we don’t store API keys:

• PagerDuty (Team+)
• Datadog, Grafana, Slack, xMatters (Enterprise)

Privacy

• Claude commercial API — customer content not used for model training
• Saved RCAs live in your account (Firebase)

Stack

React, FastAPI, Claude, Firebase, Clerk, Stripe — frontend on Vercel, API on Railway.

Ask

If you write RCAs today:

1. What sections does your org require that generators usually miss?
2. Would you use an AI draft as a starting point, or only for formatting?

Try it: https://www.rcaiapp.com

I’ll be in the comments — happy to talk architecture, quotas, or corporate VPN allowlists.

Last quarter at Zeppelin Labs, we shipped Orchestrator-15 — a multi-agent documentation generation platform that takes a codebase or idea spec and produces production-grade technical documentation using coordinated AI agents.

This post covers the architecture, the mistakes, and the specific patterns that made multi-agent coordination actually work in production. Not a tutorial — a war story.

Why Multi-Agent, Not Just One Big Prompt?

The naive approach to AI documentation generation is one giant prompt: “here’s my codebase, write the docs.”

It fails for the same reason you wouldn’t ask one person to simultaneously be a technical writer, an API analyst, a diagram designer, and an editor. Context windows are finite. Tasks have different optimization targets. And a single agent trying to do everything produces mediocre output across the board.

The multi-agent approach assigns specialized roles:

• Analyzer Agent — reads the codebase structure, identifies modules, maps dependencies
• Writer Agent — takes structured analysis output and produces prose documentation
• Formatter Agent — applies templates, ensures consistency, handles cross-references
• Reviewer Agent — checks completeness, flags gaps, scores output quality
  Each agent is good at one thing. The orchestrator coordinates them in sequence — and sometimes in parallel.

The Architecture

Input (codebase / spec)
        │
        ▼
┌──────────────────┐
│  Orchestrator    │  ← decides task graph, manages state
└──────┬───────────┘
       │
   ┌───┴────────────────────────────┐
   │                                │
   ▼                                ▼
Analyzer Agent                 Context Builder
(GPT-4o, low temp)            (builds shared memory)
   │
   ▼
Writer Agent(s)          ← spawned per module, run in parallel
(Claude 3.5, temp 0.7)
   │
   ▼
Formatter Agent
(structured output)
   │
   ▼
Reviewer Agent           ← gates output quality
(GPT-4o, strict prompt)
   │
   ▼
Final Documentation

The key design decision: shared memory over message passing. Each agent reads from and writes to a shared context object rather than receiving inputs directly from the previous agent. This lets the Reviewer Agent access the Analyzer’s raw output without it being filtered through the Writer — which turned out to be critical for catching documentation that technically read well but missed important implementation details.

The State Machine

Each document module moves through states:

type ModuleState =
  | 'pending'
  | 'analyzing'
  | 'writing'
  | 'formatting'
  | 'reviewing'
  | 'approved'
  | 'failed';

interface DocumentModule {
  id: string;
  name: string;
  state: ModuleState;
  analyzerOutput?: AnalysisResult;
  draft?: string;
  formattedDraft?: string;
  reviewScore?: number;
  reviewFeedback?: string;
  retryCount: number;
}

Modules that fail the Reviewer Agent’s quality gate (score < 0.75 on our rubric) get re-queued to the Writer Agent with the review feedback included in the prompt. We cap retries at 3 before flagging for human review.

This retry loop was the single biggest quality improvement we made. First-pass writer output approved directly produced documentation that was grammatically fine but structurally shallow. With the reviewer feedback loop, output quality jumped substantially — especially for complex modules.

Parallelism: Where It Works and Where It Breaks

Writer Agents can run in parallel — each module is independent. We spawn up to 8 concurrent Writer Agents using Promise.allSettled:

async function writeModulesInParallel(
  modules: DocumentModule[],
  context: SharedContext
): Promise<DocumentModule[]> {
  const chunks = chunkArray(modules, 8); // max 8 concurrent
  const results: DocumentModule[] = [];

  for (const chunk of chunks) {
    const settled = await Promise.allSettled(
      chunk.map(module => writerAgent.process(module, context))
    );

    for (const result of settled) {
      if (result.status === 'fulfilled') {
        results.push(result.value);
      } else {
        // mark failed, will retry with orchestrator
        results.push(markFailed(chunk[settled.indexOf(result)]));
      }
    }
  }

  return results;
}

What doesn’t parallelize well: anything that needs global consistency. The Formatter Agent must run sequentially because it maintains a cross-reference map — if two formatter instances run concurrently they produce conflicting internal link structures. We tried distributed locking on the reference map. It was brittle. Sequential formatting was the right call.

Prompt Architecture: The Part Nobody Talks About

The agents are only as good as their prompts. Our production prompts have four sections:

1. Role definition — what this agent is, what it optimizes for, what it explicitly ignores

2. Input schema — structured description of what the agent receives

3. Output schema — strict JSON format the agent must produce

4. Failure modes — explicit instructions for what to do when input is ambiguous, incomplete, or contradictory

The failure mode section was added after production. Agents without it hallucinated confidently when given ambiguous input. Agents with explicit failure mode instructions instead returned structured { "status": "needs_clarification", "question": "..." } responses that the orchestrator could handle gracefully.

The GitHub Copilot SDK Integration

Orchestrator-15 uses the GitHub Copilot SDK for the Analyzer Agent specifically — the SDK’s code-understanding capabilities are significantly stronger than general LLM prompting for structural code analysis. It can identify:

• Public API surfaces vs. internal implementation details
• Dependency graphs between modules
• Comment density and existing documentation coverage
• Test coverage as a proxy for module stability
  The Analyzer feeds this structured analysis to the Writer Agent, which dramatically reduces hallucinated API signatures — one of the most common failures in pure-LLM documentation generation.

What We’d Do Differently

Use structured outputs from the start. We started with free-form text outputs and added JSON schemas later. Every agent refactor was painful because downstream agents had built implicit assumptions about output format. Define your schemas before writing a single agent prompt.

Build the reviewer first. We built it last. If we’d built the quality rubric and reviewer first, we would have caught bad writer prompt patterns in day 1 instead of week 4.

Token budgets per agent. Without explicit token limits per agent, the Writer Agent would occasionally produce exhaustive output for simple modules and thin output for complex ones. Calibrating per-module token budgets based on the Analyzer’s complexity score (lines of code, dependency count) significantly improved consistency.

The Repo

Orchestrator-15 is open source. You can find it on the Zeppelin Labs GitHub. We’re actively developing it — issues and PRs welcome.

If you’re building multi-agent systems and want to compare notes, drop a comment below or reach out through zeppelinlabs.digital.

Built at Zeppelin Labs — a software development studio building SaaS products, AI systems, and automation platforms from Islamabad, Pakistan.

Over the past few months, I have been building a small AI news brief called DeepSignal.

The idea started from a simple personal frustration:

I was reading X, Hacker News, arXiv, OpenAI and Anthropic blogs, product launch pages, newsletters, and company updates every day, but still felt like I was either missing important AI news or wasting time on low-signal updates.

So I built a small system that does three things:

1. Collects AI-related updates from multiple sources
2. Scores each story with a transparent 0–100 signal score
3. Publishes a daily and weekly brief

The product is not technically complex, but the workflow taught me a lot about building AI-assisted content products, SEO for dynamic sites, and the difference between summarizing information and filtering information.

This is a breakdown of the stack, architecture, and lessons learned.

The stack

The current stack is intentionally simple:

Frontend: Next.js 15
Database: Supabase
Hosting: Vercel
AI processing: GPT-4o-mini
Content model: Articles, sources, tags, guides, weekly briefs
SEO: sitemap, canonical URLs, RSS, structured pages

I wanted to keep the system cheap and easy to maintain because this is a solo project.

The rough monthly cost is still low. Vercel handles deployment and hosting, Supabase handles the database, and GPT-4o-mini is used for scoring and classification rather than heavy generation.

The main goal was not to build a complicated AI pipeline.

The goal was to build a reliable workflow that could turn noisy inputs into useful outputs.

The basic architecture

The system has a simple flow:

Sources
   ↓
Fetch / import
   ↓
Normalize article data
   ↓
AI relevance check
   ↓
Signal scoring
   ↓
Tagging and categorization
   ↓
Publish article pages
   ↓
Generate daily / weekly briefs
   ↓
Expose guides, RSS, sitemap

At a high level, each story becomes a structured object:

type Article = {
  id: string;
  title: string;
  url: string;
  source: string;
  summary: string;
  publishedAt: string;
  aiRelevanceScore: number;
  signalScore: number;
  tags: string[];
  category: string;
  canonicalUrl: string;
  isIndexable: boolean;
};

The most important field is not the summary.

It is isIndexable.

That one field ended up being more important than I expected.

Why filtering matters more than summarizing

At first, I thought the main problem was summarization.

Take a long article, summarize it, and users save time.

But after building the first version, I realized summarization alone does not solve the real problem.

A summary tells you:

What does this article say?

But users usually need to know:

Should I care?
Why does this matter?
Is this actually about AI?
Is this a durable signal or just a temporary headline?
Is this more important than the other 50 updates today?

That changed the product direction.

Instead of only generating summaries, the system needed to decide what should be included, ranked, grouped, and excluded.

For an AI news product, filtering is not a minor feature.

Filtering is the product.

The signal score

Each story gets a 0–100 signal score.

The score is not meant to be perfect. It is a transparent ranking system that helps explain why a story may matter.

A story can score higher based on signals like:

- source quality
- AI relevance
- novelty
- technical depth
- business impact
- research importance
- company importance
- cross-source confirmation
- relevance to builders, researchers, or operators

A simplified scoring idea looks like this:

type ScoreInput = {
  sourceWeight: number;
  aiRelevance: number;
  novelty: number;
  technicalDepth: number;
  marketImpact: number;
  researchValue: number;
  companyImportance: number;
};

function calculateSignalScore(input: ScoreInput) {
  const score =
    input.sourceWeight * 0.15 +
    input.aiRelevance * 0.25 +
    input.novelty * 0.15 +
    input.technicalDepth * 0.15 +
    input.marketImpact * 0.1 +
    input.researchValue * 0.1 +
    input.companyImportance * 0.1;

  return Math.round(Math.min(100, Math.max(0, score)));
}

The exact formula can change, but the principle matters:

I wanted users to feel that the ranking had a visible logic, not just a black-box AI label.

That was one of the biggest lessons:

A simple transparent scoring system can be more trustworthy than a more complex but invisible AI ranking.

Using GPT-4o-mini

I use GPT-4o-mini mostly for classification, scoring support, and short summaries.

The AI tasks are intentionally narrow:

- Is this article actually AI-related?
- What category does it belong to?
- What are the key takeaways?
- Is the story relevant to models, agents, research, hardware, infrastructure, regulation, or adoption?
- What tags should it receive?
- What score explanation should be shown?

I try not to use AI as a generic content generator.

Instead, I use it as a structured processing layer.

A simplified prompt pattern looks like this:

You are classifying an AI industry news article.

Return JSON only.

Evaluate:
1. AI relevance from 0 to 100
2. Signal strength from 0 to 100
3. Primary category
4. 3 to 5 tags
5. One-sentence reason why this story matters
6. Whether this story should be indexable for search

Article:
Title: ...
Source: ...
Excerpt: ...
URL: ...

The important part is forcing structured output.

For this kind of workflow, predictable JSON is more useful than beautifully written prose.

Supabase data model

The database is simple.

Core tables:

articles
sources
tags
article_tags
daily_briefs
weekly_briefs
guides
guide_articles

The articles table stores the normalized content.

The sources table stores source metadata and source quality.

The tags table keeps topic structure clean.

The guides table is for evergreen topic pages, such as:

AI agents
AI coding tools
AI research papers
OpenAI updates
Anthropic Claude updates
NVIDIA AI chips
AI hardware

This guide layer became important later for SEO.

A chronological feed is useful for freshness, but guide pages are better for long-term search and topic authority.

Next.js page structure

The site uses a few main page types:

/
Homepage

/articles/[slug]
Individual article pages

/guides
Guide index

/guides/[slug]
Evergreen topic pages

/weekly
Weekly AI brief

/tags/[slug]
Core topic pages

/sources/[slug]
Selected source pages

Not every page deserves to be indexed.

That became one of the most important SEO decisions.

SEO lesson: not every page should be in the sitemap

Early on, I made the mistake of thinking more indexed pages would be better.

It was not.

When a site has too many low-quality, thin, duplicate, or off-topic pages, search engines can get confused about what the site is actually about.

For an AI news site, this matters a lot because source feeds can easily include AI-adjacent but irrelevant content.

So I added stricter sitemap rules.

The sitemap should include:

- homepage
- about page
- guides
- high-quality guide pages
- weekly brief
- selected high-quality article pages
- selected core tag pages

The sitemap should not include:

- saved pages
- subscribe pages
- internal API routes
- search result pages
- parameter URLs
- low-quality tag pages
- non-AI articles
- thin source pages
- duplicate daily feed pages

The rule I use now is simple:

Only put a URL in the sitemap if it is:

- canonical
- indexable
- useful as a search landing page
- relevant to the core AI topic
- not thin or duplicated

This helped clean up the site’s search profile.

Canonical URLs and UTM links

For promotion, I use UTM links like:

https://ai-deep-signal.com/weekly?utm_source=x&utm_medium=social&utm_campaign=weekly

or:

https://ai-deep-signal.com/?utm_source=reddit&utm_medium=social&utm_campaign=launch

But the canonical URL must always point to the clean version:

https://ai-deep-signal.com/weekly
https://ai-deep-signal.com/

That avoids turning campaign URLs into duplicate SEO pages.

For a dynamic site, this is easy to overlook.

Tracking URLs are for analytics.

Canonical URLs are for search engines.

They should not be mixed.

Why I added guides and weekly briefs

The first version of the site was mostly a feed.

That worked, but it had a problem:

Feeds are good for browsing.

Guides are better for understanding.

So I added topic-based guides and a weekly brief.

The weekly page is for people who want a quick summary of what mattered this week.

The guide pages are for evergreen themes that should grow over time.

For example:

/guides/what-are-ai-agents
/guides/best-ai-coding-agents
/guides/ai-research-papers-this-week
/guides/nvidia-ai-chip-news
/guides/openai-news

This gives the site a more stable structure:

Homepage
  ↓
Guides
  ↓
Topic pages
  ↓
Related articles

That structure is much better than only having a reverse-chronological feed.

Deployment on Vercel

Vercel is a good fit for this kind of project because most of the site is content-oriented.

The project benefits from:

- fast deployments
- preview deployments
- automatic HTTPS
- good Next.js support
- serverless functions for lightweight API work
- ISR / caching options

But I avoid using Vercel for heavy background work.

If the project grows, I would move heavier jobs to a separate worker or queue system.

For now, Vercel + Supabase is enough.

What I would improve next

There are still many things I would improve.

Better deduplication

AI news often appears in multiple places. The same story can show up as a company blog post, a tweet thread, a newsletter item, and a Hacker News discussion.

Better clustering would make the brief cleaner.

Better source weighting

Not all sources should have equal authority. A research paper, company announcement, social post, and rewritten news article should be weighted differently.

Better guide pages

The guide pages should become more like living topic trackers, not just lists of related articles.

Each guide should eventually include:

- topic explanation
- latest updates
- important companies
- relevant research
- key risks
- related stories
- last updated date

Better scoring explanations

A score is only useful if users understand it.

I want each article to explain not just the score, but the reason behind the score.

What I learned

A few lessons stood out.

1. Filtering is harder than summarizing

Summarization is relatively easy now. Deciding what deserves attention is much harder.

2. SEO quality matters more than SEO volume

More pages are not always better. Cleaner, more relevant pages are better.

3. Topic pages are more durable than feeds

Feeds create freshness. Guides create long-term value.

4. Transparent AI systems feel more trustworthy

Users do not need a perfect score, but they do need to understand why a score exists.

5. The workflow around the model is the real product

The AI model is only one part. The source selection, scoring rules, publishing flow, SEO structure, and user experience matter just as much.

Final thoughts

This project started as a small personal tool because I was tired of reading too many AI sources every morning.

But it turned into a useful lesson:

AI products do not always need to generate more content.

Sometimes the better product is the one that helps people ignore more content.

That is what I am trying to build with DeepSignal: a cleaner way to follow AI news, research, agents, models, and infrastructure without the daily noise.

The site is here:

https://ai-deep-signal.com/?utm_source=devto&utm_medium=article&utm_campaign=build_log

The weekly brief is here:

https://ai-deep-signal.com/weekly?utm_source=devto&utm_medium=article&utm_campaign=build_log

I would love feedback from other developers:

Would you trust a transparent signal score for news ranking?
Or would you rather see a purely editorial brief without scoring?