ACT-GP Browser & Search Engine Concept

ACT-GP Browser & Search Engine Concept

Building a New Web: The ACT-GP Browser & Search Engine Concept

How a modular AI-first pipeline becomes a complete, privacy-focused gateway to explore, index, and understand the internet.

Repo: https://github.com/pacobaco/act-gp

The modern web is increasingly shaped by artificial intelligence, distributed systems, multimodal content, and personalized interfaces. Yet the tools we use to access that web—browsers and search engines—have remained surprisingly static for more than a decade. We type queries in a box, we scan through links, and we leave behind trails of behavioral data that we rarely control. The companies controlling search and browsers centralize vast insight, while the users performing that search end up as the product.

What would it look like to reimagine the entire web access stack—browser, search engine, index, pipeline, and AI layer—from the ground up?

This article introduces a detailed blueprint for such a system: the ACT-GP Browser and Search Engine, based on principles and scaffolding from the ACT-GP repository. ACT-GP provides a lightweight, modular, and extensible foundation for acquiring, cleaning, tokenizing, modeling, and deploying content pipelines. With the right extensions, it can drive a privacy-centric, explainable, AI-augmented search engine and browser.

1 — Why Build a New Browser and Search Engine?

Modern search engines optimize for monetization. Browsers optimize for ecosystem lock-in. Neither optimizes for user empowerment.

Users increasingly want:

  • Transparent algorithms, not black-box ad stacks.
  • Semantic search, not keyword guessing.
  • Personal control, not covert telemetry.
  • Local AI, not cloud profiling.
  • Multilingual search, not English-only bias.
  • AI-assisted browsing, not AI that replaces exploration.

ACT-GP already includes the fundamental tools needed to build this future: acquisition scripts, preprocessing utilities, model training, multilingual capabilities, and clean deployment patterns.

2 — Architecture Overview: The ACT-GP Search Stack

A modern search engine consists of eight major components:

  1. Crawler
  2. Content Extraction
  3. Cleaning & Normalization
  4. Tokenization & Chunking
  5. Vectorization & Embeddings
  6. Indexing
  7. Ranking
  8. Answer Generation & UI

2.1 Crawler & Acquisition

The crawler extends ACT-GP’s acquisition layer into a high-efficiency fetcher using asynchronous scheduling and domain-aware politeness. Each crawl stores:

  • Raw HTML
  • HTTP headers
  • Timestamps
  • Structured metadata (schema.org, OpenGraph, JSON-LD)

2.2 Cleaning & Normalization

ACT-GP’s preprocessing becomes a structured cleaning pipeline. It removes boilerplate, detects language, strips ads, and cleans navigation clutter. It prepares content for embedding models and improves ranking quality.

2.3 Tokenization & Chunking

Using ACT-GP’s tokenizer utilities, content is transformed into structured tokens. Chunks maintain metadata, enabling smarter ranking and citation.

2.4 Embeddings & Vector Store

Chunks are embedded using SentenceTransformers, OpenAI embeddings, or ACT-GP fine-tuned models. Two indices are maintained:

  • Lexical index — BM25, keyword matching.
  • Vector index — semantic similarity across languages.

2.5 Ranking

Ranking combines:

  • BM25 lexical relevance
  • Semantic similarity
  • Freshness signals
  • Domain trust
  • Optional personalization (stored locally only)

Every result includes explainable ranking metadata—no hidden signals.

2.6 Answer Generation

Using AI models, the engine can summarize, translate, explain, or contextualize content. However, citations are always required.

3 — The ACT-GP Browser: Human-First Design

3.1 Core Features

  • Unified address + search bar
  • Reader Mode with AI summarize
  • On-page AI assistant
  • Citation transparency panel
  • Offline search mode
  • Knowledge Map sidebar

3.2 Privacy Architecture

  • No default telemetry
  • Encrypted local profiles
  • Optional local-only AI inference
  • No forced cloud sync
  • No built-in third-party trackers

3.3 AI-Enhanced Browsing

  • Contextual Q&A
  • Instant translation
  • Summaries for long pages
  • Concept graph extraction
  • Code explanations for developers

4 — Expertise Location

Using techniques adapted from classic expertise-locator systems, the platform can identify experts for any topic.

How it works:

  • Detect names via NER
  • Extract keywords & concepts
  • Compute co-occurrence graphs
  • Rank people on topic expertise

Useful for enterprise, open-source communities, and research networks.

5 — Governance, Licensing & Transparency

5.1 Licensing Indicators

Every indexed document shows its license: MIT, CC, GPL, Proprietary, or Unknown.

5.2 Data Provenance

Every result displays crawl timestamp, ranking reasons, and trust tier.

5.3 Takedown Policies

Authors and domain owners may request corrections or removal.

6 — Implementation Roadmap

Phase 1

  • Fork ACT-GP
  • Build expanded crawler
  • Extend preprocessing pipeline

Phase 2

  • Add dual indexing (lexical + vector)
  • Develop ranking layer
  • Deploy search API

Phase 3

  • Build desktop & mobile browser
  • Add reader mode & sidebar
  • Integrate AI tools

Phase 4

  • Add expertise locator
  • NER + co-occurrence models

Phase 5

  • Governance portal
  • Public beta

7 — What the ACT-GP Browser Represents

This is more than a browser or search engine. It is a new interface for the web, grounded in:

  • Semantic understanding
  • Multilingual intelligence
  • Transparent ranking
  • Privacy-first architecture
  • Human-guided AI tools

8 — Final Thoughts: A Web Worth Exploring Again

The modern web is flooded with content, including low-quality AI-generated material. Traditional search engines tighten their opacity, increase ads, and obscure data flows.

The ACT-GP Browser & Search Engine points in a different direction:

  • Open design
  • Accountable algorithms
  • Citation-driven AI
  • User control over data
  • Multilingual core
  • Explainable search

This system doesn’t just show pages—it shows relationships, expertise, knowledge, and the structure of ideas across languages and domains.

A better web isn’t just possible; it’s buildable. And ACT-GP provides the blueprint.

Comments

Post a Comment

Popular posts from this blog

Survival Guide: Overcoming Food Insecurity in College

Below is an expanded, 2500-word blog post version of the survival guide on food insecurity for college students. It’s structured to be engaging, empathetic, and comprehensive, with practical tips, real-world examples, and actionable advice. The tone remains supportive and relatable, tailored for college students, with added depth through personal anecdotes, detailed strategies, and broader context on systemic issues. The post is designed to be informative, inspiring, and empowering, encouraging students to take control of their situation while advocating for change. Survival Guide: Overcoming Food Insecurity in College Food insecurity—lacking consistent access to enough nutritious food to live a healthy, active life—is a silent struggle for many college students. With tuition, textbooks, rent, and other expenses eating up limited budgets, putting food on the table can feel like an impossible task. Studies estimate that 23-30% of college students experience food insecurity, skipping...
Read more

ACT-GP White Paper: Keyword-Prompt AI Model (Multilingual)

ACT-GP White Paper: Keyword-Prompt AI Model (Multilingual) White Paper: Developing a Keyword-Prompt AI Model from Public Repositories and Text Sources (Multilingual) Executive Summary The AI Code and Text Generation Platform (ACT-GP) is designed to translate keyword prompts into actionable outputs, such as code snippets, function templates, tutorials, and explanatory text, using public repositories and multilingual text sources . By incorporating structured data management, tokenization, multilingual support, and transparent provenance, ACT-GP mitigates risks associated with opaque models while delivering high-quality outputs. This white paper provides a comprehensive overview of the end-to-end process , including the nine critical steps: acquisition, cleaning, structuring, tokenization, model selection, training, evaluation, deployment, governance, and internationalization features for multilingual corpora. --- 1. Data Acquisition Sources include: Code Repositories...
Read more

The Future of Search Is Agentic: From QueryNet to Autonomous AI Agents (2025 Edition)

Image
The Future of Search Is Agentic: From QueryNet to Autonomous AI Agents (2025 Edition) The Future of Search Is Agentic: From QueryNet to Autonomous AI Agents (2025 Edition) Published December 11, 2025 • ~5,000 words • 22 min read Table of Contents 1. Introduction: The Three Waves of Search 2. The Calculus of Human Behavior 3. QueryNet: Stateful Prediction from Search Queries 4. Traditional IR vs. User-Centric Models 5. The Agentic Revolution (2025 Landscape) 6. Agentic Training Pipelines for QueryNet 7. Stateful vs Stateless Models in Real-World Query Systems 8. Where We Go From Here 1. Introduction: The Three Waves of Search Search, as we knew it for the first thirty years of the web, is dead. We are living through the third great transformation of information retrieval: 1995–2010 – Keyword matching & PageRank (document-centric) 2010–2024 – User-centri...
Read more