Evidence Media

In spring 2025, I launched Evidence Media, a groundbreaking platform that automates the curation and publication of independent online news, delivering sourced posts on X and detailed articles on Substack. Born from my desire to counter misinformation, this solo project evolved from a personal idea into an essential resource for informing the North American public.

Evidence Media is a fully automated, AI-powered news pipeline designed to curate, aggregate, and distribute information from independent online sources. It publishes daily sourced updates on X (formerly Twitter) and long-form articles on Substack.

Since the COVID-19 pandemic, public trust in mainstream media has been severely undermined due to widespread collusion between institutions, governments, and news organizations. This convergence gave rise to a homogenized narrative, accompanied by systemic censorship—now widely referred to as the censorship industrial complex. In response, a growing number of citizens have turned to independent sources of information: freelance journalists, niche newsletters, alternative podcasts, and social platforms like X.

But in this decentralized landscape, staying genuinely informed has become increasingly time-consuming. Cross-referencing, verifying, and filtering a rising flood of scattered information is a task few people have time for.

That’s where Evidence Media comes in. Its mission is to filter and aggregate the most relevant and impactful independent news for North American audiences (Canada and the U.S.). The platform covers eight key categories: Business & Economics, Current Affairs, Environment, Health, International Affairs, Politics, Science & Technology, and Society.

Every piece of information shared is accompanied by its original source, allowing readers to assess its relevance, verify its accuracy, or explore further if desired. Unlike mainstream outlets bound by top-down editorial lines—or some independent sources that lack rigorous sourcing—Evidence Media is built on transparency, reliability, and methodological integrity.

The project was initiated in early spring 2025, with its first official version launched by late spring of the same year.

Evidence Media was a fully solo project. I handled every layer from vision to execution:

• Product Manager: I defined the mission, editorial identity, positioning, target personas, and long-term vision.
• Backend Development: I built all automation scripts in Python, structured a modular architecture, and managed data workflows.
• AI Integration: I orchestrated a multi-model pipeline using OpenAI, Perplexity, and xAI APIs to generate contextualized, sourced content, as their relative media.
• Advanced Automation: I implemented GitHub Actions for scheduling and deployment, secured secrets with HashiCorp Vault, and designed a system built for scale.

From concept to iteration, from the first line of code to editorial strategy, I drove every decision with one clear goal: to build a resilient, automated, and trustworthy news platform that serves the public good.

The creation of Evidence Media was born from a clear realization: staying properly informed has become both complex and time-consuming. This growing information gap allows politicians, institutions, and influential actors to operate without real checks and balances, a serious threat to any democratic society. Where journalism once served as a safeguard, traditional media is now largely owned by the very entities it should be holding accountable, breaking the bond of public trust.

Evidence Media is a direct response to this imbalance, built on three core pillars: a personal observation, a tangible need for clarity in a fragmented media landscape, and a creative drive to structure independent information in a way that is both accessible and credible.

My studies at HEC Montréal shaped the project's strategic vision and business model, while my hands-on experience with Wise Duck Dev GPTs and Jean The Writer equipped me with the expertise in artificial intelligence and automation needed to develop a solution that is robust, reliable, and fully scalable.

Evidence Media was born from a clear mission: to deliver independent, sourced, reliable, and relevant information, with maximum signal and minimal noise. I began with a functional MVP (Version 1) focused on automation, content quality, and editorial integrity.

Rather than building around predefined personas or chasing algorithmic trends, I created the media outlet I wished already existed—one aligned with my core values, free from the noise of traditional news cycles, and respectful of the reader’s intelligence.

This product-centric and values-driven approach naturally led to the development of a unique political doctrine, which became the project’s editorial backbone—ensuring coherence, honesty, and civic responsibility across every piece of published content.

Operational workflows were designed in response to the constraints of the platforms used. For example, X’s free API limits posting to 17 publications per day—this technical limitation directly shaped the platform’s daily editorial cadence. Substack, on the other hand, emerged as the ideal channel for long-form daily articles, organized across eight key content categories. The goal: to help readers build a cross-disciplinary, contextual, and informed understanding of current events.

Instead of chasing conventional growth metrics, I chose to focus on strategic milestones: reaching 500 verified subscribers and 5 million impressions on X within the first year to unlock monetization, then reinvesting in the paid API. On Substack, I made the decision to keep all content fully free for a year to build reader trust before introducing a premium model.

The methodology was agile and iterative—constantly refining the formats, editorial tone, and automation workflows based on personal observations, user feedback, comments, and algorithmic signals.

Finally, the system was designed for resilience and scale: 100% automated, agent-ready, and structured to evolve alongside the capabilities of the underlying APIs.

• Languages/Frameworks: Python, Flask, YAML, Shell Scripting
• AI: OpenAI, Perplexity, Xai
• APIs: X API v2, Xai API, Perplexity API, OpenAI API, Google API
• Automation: GitHub Actions
• Security: HashiCorp Vault
• Scraping: BeautifulSoup, Selenium
• Others: Tweepy, PyVirtualDisplay

I chose Python for its rich ecosystem in automation, especially tools like Selenium and BeautifulSoup. What started as a new technical venture quickly became a passion. All the technologies I used are reliable, well-documented, free (except for the AI APIs), and perfectly suited for automated scripting.

AI integration (e.g., xAI) and secure automation (via GitHub Actions and Vault) are central pillars of the project.

Evidence Media doesn’t have a traditional UI, its user experience is entirely content-driven. I focused on crafting a readable, reliable, and recognizable content format, inspired by the most effective X and Substack accounts. Every post is designed to deliver immediate clarity and traceable credibility by citing original sources, similar to academic footnotes, helping users assess news at a glance or explore further.

The main UX challenge was LLM hallucinations and inconsistency on politically sensitive topics. I addressed this through aggressive prompt engineering, and custom constraints, significantly reducing factual drift. Still, certain topics remain inherently unstable in current models, underscoring the importance of transparent sourcing.

To support discoverability and habit-building, I introduced consistent formatting across X posts (short, sourced, high-signal) and Substack articles (longer, categorized, contextualized), reinforcing trust and boosting engagement over time.

Evidence Media runs entirely from a GitHub-hosted codebase using GitHub Actions for scheduled, event-driven automation. While it doesn't rely on traditional deployment platforms (like Vercel or AWS), its architecture is optimized for resilience, modularity, and infinite scalability.

Each functional component, from scraping to AI generation to publishing, is encapsulated in discrete scripts that can be scaled horizontally or triggered independently, as needed.

The system supports CI/CD via GitHub Actions, while monitoring and performance analytics are handled through native platform dashboards (X and Substack). The only true limitations are API usage quotas (OpenAI, Perplexity, X API, etc.), which define throughput, but the architecture itself is capable of 24/7 continuous publishing at industrial scale with minimal adjustments.

In short, the pipeline is not just automated, it’s built to grow.

• V1: X pipeline (short-form, sourced posts).
• V2: Content redesign, improved cadence and formatting.
• V3: Substack integration (daily sourced articles).
• V4: Toward a fully autonomous 24/7 pipeline.

Next: progress from Chain of Thoughts (CoT) pipelines to agentic AI, and once a sufficient vetted corpus exists, fine-tune a model on the curated dataset to improve cross-story linking and historical context.

Evidence Media’s X account quickly reached over 500 organic followers, with consistent growth driven by high-quality, source-backed content. Substack adoption is slower but steadily increasing. Audience feedback reflects the polarized landscape of modern media consumption: while many users appreciate the transparency, reliability, and AI-powered curation (notably the “Evidence” AI editor persona), others remain skeptical of AI involvement.

Engagement varies by topic, but the overall reception validates the project’s mission, offering verifiable, independent information in a time of institutional distrust. The system’s success further confirmed that automation, sourcing, and fast iteration are key levers for building trust and reach in digital media.

Evidence Media wasn’t just another automation project, it was a deep dive into building a living, breathing AI-powered media outlet from scratch. It sharpened every dimension of my technical skill set: Python, web scraping, prompt engineering, data integrity, CI/CD pipelines, API orchestration, and cybersecurity with Vault. I built systems that are not only fast and scalable, but also resilient, verifiable, and transparent, essential traits when working in information distribution.

I learned to tame large language models in high-stakes editorial contexts, resolving hallucinations, bias, and inconsistency through layered prompt strategies and dynamic content filtering. I developed ways to ensure that AI supports human understanding rather than distorting it, preserving truth and traceability through academic-style sourcing.

But beyond the tech, this project solidified a core truth: building something truly useful doesn’t come from chasing trends or audience metrics, it comes from solving your own problem first, at scale. I built the news outlet I was searching for. The one I needed, but couldn’t find.

Most importantly, this project reaffirmed a principle I now apply everywhere: quick, thoughtful iteration beats perfection. Shipping fast, observing real-world feedback, and improving continuously is the fastest path to building reliable, high-impact systems.

Key takeaway: Think independently. Iterate rapidly. Automate relentlessly. Build solutions that you would genuinely use—and others will follow.