I’m a multi-skilled IT professional with a good all-round supervisory and technical expertise. Extensive, 20+ years of professional experience in software development allowed me to investigate computer science and software engineering inside out. During these years I built up a solid base of design patterns, software architectures and programming languages such as C/C++, Golang, Java, Python, SQL, Assembly (and many others). I worked on mission-critical and multi-channel applications, applying distributed computing, messaging, image/data processing and computer graphics techniques. I faced both architecture design and systems rearchitecting, microservices introduction and technology migration as well as company wide adoption of new technologies/methodologies multiple times.
As an entrepreneur I have built and grown teams and development organizations from the ground up (internal/out sourced/at customer site) focusing on software engineering
methodologies as well as recruiting, budget/financial control and operations support.
I am particularly interested in software testing methodologies, software quality metrics
and tools to make software development faster and better.
Currently leading the Italian development team for ScientiaMobile Inc, a Reston (US) based startup focused on image optimizing CDN and mobile detection technologies and services. Born in Dearborn Michigan and living in Italy since many years now I speak fluently both English and Italian, studied French and learned some Russian while working for some time for a Olivetti/Aeroflot project.
The Open Licensing Standard for AI Crawlers – Giving publishers a voice and AI a smarter path forward — beyond scraping vs. paywalls to balanced collaboration https://peekthenpay.org/#how-it-works expect one of these new standards per month for the next months.
How AI internet scraping is evolving, current techniques used : – Direct HTTP Crawlers (Traditional Crawlers) : GPTBot, ClaudeBot, Meta-ExternalAgent, Google-Extended, Bytespider, Amazonbot, Applebot-Extended, CCBot – Cloud Browser Infrastructure (Browser-as-a-Service) : Browserbase, Hyperbrowser – Web Scraping & Data Extraction Platforms : Firecrawl, Apify, Zyte – Browser-driven web agents : Comet (Perplexity), Dia (The Browser Company) – Real-Time Fetchers (On-Demand) : ChatGPT-User, OAI-SearchBot, Claude-User, Perplexity-User
Create content and audiences; provide advertising inventory (impressions)
Goal
Maximize revenue per page view (RPM/CPM); balance UX with monetization
Top 5 Companies
Google (YouTube), Meta, Amazon, News Corp, Condé Nast
Business Model
Ad revenue (CPM/CPC/CPA share), subscriptions, hybrid. Typically keep 60-80% of programmatic revenue after tech fees
Software Components
CMS (WordPress, Drupal), audience development tools, DMP/CDP for 1st party data, consent management
2. PUBLISHER AD SERVER
Attribute
Details
Actor
Publisher-Side Ad Server
Role
Manage, prioritize, and deliver ads across direct-sold and programmatic demand sources
Goal
Maximize yield by selecting the highest-paying ad for each impression; enforce business rules
Top 5 Companies
Google Ad Manager (GAM), Xandr Monetize, Kevel (self-serve), Smart AdServer, Equativ
Business Model
CPM-based serving fees (GAM free tier, then volume-based); SaaS subscription for smaller players
Software Components
Commercial: Google Ad Manager, Xandr, Equativ, Smart. Open Source: Revive Adserver (legacy). Modules: Trafficking UI, ad decisioning engine, forecasting, reporting, unified auction
3. HEADER BIDDING WRAPPER / PREBID
Attribute
Details
Actor
Header Bidding Wrapper
Role
Run parallel auctions across multiple SSPs before calling the ad server; maximize competition
Goal
Increase publisher yield by enabling simultaneous bidding; reduce SSP monopoly power
Top 5 Companies
Prebid.org (standard), Amazon TAM/UAM, Google Open Bidding, Index Exchange Wrapper, PubMatic OpenWrap
Business Model
Prebid.js is free/open-source. Managed services charge fees or require using their SSP. Prebid Server hosts charge tech fees
Software Components
Open Source: Prebid.js (client), Prebid Server (server-to-server). Commercial wrappers: TAM, OpenBidding. Modules: Adapters (per SSP), currency conversion, consent modules, analytics adapters, user ID modules
Neutral marketplace connecting multiple SSPs and DSPs; facilitate real-time auctions
Goal
Maximize liquidity; enable price discovery; provide transaction infrastructure
Top 5 Companies
Google AdX (dominant), Xandr (Microsoft), Magnite, OpenX, Smaato (Verve Group)
Business Model
Transaction fee (often bundled with SSP); typically 10-20%
Software Components
RTB auction engine, QPS infrastructure, fraud filtering, OpenRTB/oRTB 2.6 compliance, deal management. Note: Exchange vs SSP distinction has blurred; most SSPs function as exchanges
7. CONSENT MANAGEMENT PLATFORM (CMP)
Attribute
Details
Actor
CMP (Consent Management Platform)
Role
Collect, store, and signal user privacy consent for GDPR/CCPA/GPP compliance
CPM-based resolution fees; licensing; bundled with other services
Software Components
Commercial: LiveRamp ATS, UID2, ID5. Open Standards: UID2 (open-source framework), SharedID. Modules: Identity graph, resolution API, Prebid User ID modules, first-party data onboarding
9. VIDEO / CTV SPECIFIC
Attribute
Details
Actor
Video Ad Server / CTV Platform
Role
Serve and measure video ads (instream, outstream, CTV); handle VAST/VPAID
Goal
Deliver video ads with proper tracking; manage pods; measure completion rates
Top 5 Companies
Google Ad Manager (video), FreeWheel (Comcast), SpringServe, Magnite CTV, Innovid
Business Model
CPM-based serving fees (higher than display); SaaS subscription for ad servers
Software Components
Commercial: FreeWheel, SpringServe, Innovid. Standards: VAST 4.2, VPAID (deprecated), SIMID, OMID. Modules: Video player integration, pod management, server-side ad insertion (SSAI), frequency capping across screens
Beads is a lightweight, graph-based issue tracker designed specifically for AI coding agents (like Claude, GPT-4, etc.) rather than human developers https://github.com/steveyegge/beads
Genkit Go 1.0 seems promising : – Type-safe AI flows with Go structs and JSON schema validation – Unified model interface supporting Google AI, Vertex AI, OpenAI, Ollama, and more – Tool calling, RAG, and multimodal support – Rich local development tools with a standalone CLI binary and Developer UI – AI coding assistant integration via genkit init:ai-tools command for tools like the Gemini CLI
Being non deterministic LLMs based AI Agents are un-testable (in sw engineering current terms) : the only criteria to evaluate anwsers is “LGTM” .. “A pragmatic guide to LLM evals for devs” https://newsletter.pragmaticengineer.com/p/evals
PACESETTERS is a powerful alliance of 15 partners of diverse scope, scale and focus. The consortium draws on long-term experience, outstanding competences and specific expertise. https://pacesetters.eu/about
“Notably, during Neo’s demo with the WSJ, the robot wasn’t performing any tasks autonomously. However, Børnich says Neo will perform “most household tasks autonomously” when it launches next year, noting that the quality of work “varies and will improve dramatically very rapidly as we acquire data.” Neo Robot is cheating like all the other manufacturers right now. https://www.roadtovr.com/helper-robot-neo-vr-telepresence/
“There’s more to software development than producing a working solution. Someone needs to safeguard design intent and maintainability. Maybe as LLMs democratize coding, existing developers need to evolve into architects who curate the structure of a codebase.” https://mo42.bearblog.dev/help-my-boss-started-programming-with-llms/
Curious to see where this goes.. Subliminal Learning : Language models transmit behavioral traits via hidden signals in data https://arxiv.org/abs/2507.14805
Apollo mission audio/images in realtime (obviously we have never been to the moon, they did all this with photoshop in the 70s 🙂 ) https://apolloinrealtime.org/
Emissions fell by 4% in Q1 and 2.6% in Q2, while GDP grew by 0.3% and 1%, respectively, compared to the same quarters in 2023, according to the latest statistics. This demonstrates that climate action and economic growth can go hand in hand : https://ec.europa.eu/eurostat/en/web/products-eurostat-news/w/ddn-20241115-2
For true ease of use, you’ll want to start with one of these applications. They package the models and provide a simple interface (either graphical or a single command) to get you started in minutes, with no coding required.
Ollama: This is arguably the easiest and most popular command-line tool. It bundles model weights, configuration, and a server into one simple package. You install Ollama, then run a single command like ollama run llama3 in your terminal to download the model and start chatting. It’s available for Windows, macOS, and Linux.
LM Studio: A fantastic desktop application with a graphical user interface (GUI). It allows you to browse and download a massive library of models (in the popular GGUF format), configure settings, and chat with the model, all within a user-friendly window. It’s perfect if you prefer not to use the command line.
GPT4All: Another great GUI-based option that is optimized to run a wide variety of quantized models on your computer’s CPU, making it accessible even without a powerful graphics card.
Top Open-Source LLMs for Personal Use
These models are great because they offer a fantastic balance of performance and manageable size, making them ideal for running on consumer hardware like modern laptops and desktops.
General Purpose & Chat
Meta Llama 3
Why it’s great: This is the current state-of-the-art open-source model. It’s incredibly capable for chatting, writing, summarizing, and coding.
Best Version for Personal Use:Llama 3 8B Instruct. The “8B” stands for 8 billion parameters. It’s the sweet spot, requiring about 8 GB of RAM/VRAM to run smoothly.
Supported by: Ollama, LM Studio, GPT4All.
Mistral 7B
Why it’s great: Before Llama 3, this model was the king of its size class. It’s known for being very fast, coherent, and excellent at following instructions and coding, often outperforming larger models.
Best Version for Personal Use:Mistral 7B Instruct. It’s very lightweight and efficient.
Supported by: Ollama, LM Studio, GPT4All.
Google Gemma
Why it’s great: Developed by Google, these models are built with the same technology as the powerful Gemini models. They are solid all-rounders.
Best Version for Personal Use:Gemma 7B for powerful machines, or Gemma 2B for less powerful ones (like laptops without a dedicated GPU).
Supported by: Ollama, LM Studio.
Specialized & Lightweight Models
Microsoft Phi-3
Why it’s great: A new generation of “small language models” (SLMs) that pack a surprising punch. They are designed to run very efficiently on low-resource devices, including phones.
Best Version for Personal Use:Phi-3 Mini 3.8B. It performs at a level far above what you’d expect from such a small model, making it perfect for laptops or older desktops.
Supported by: Ollama, LM Studio.
Qwen2 (from Alibaba Cloud)
Why it’s great: A very strong family of models with excellent multilingual capabilities and strong performance in both chat and coding. They come in many sizes.
Best Version for Personal Use:Qwen2 7B is a great Llama 3 alternative. For lower-spec machines, Qwen2 1.5B is a fantastic and fast option.
Supported by: Ollama, LM Studio.
What You Need to Consider
VRAM (GPU Memory): This is the most important factor. The model needs to be loaded into your graphics card’s memory. A model’s size (e.g., 7B) roughly corresponds to the VRAM needed in GB (e.g., a 7B model needs about 7-8 GB of VRAM).
Quantization: This is a technique to shrink models to run on less powerful hardware, with a small trade-off in performance. Tools like LM Studio and Ollama handle this for you automatically, downloading pre-quantized versions so you don’t have to worry about it.
CPU vs. GPU: While you can run these models on your CPU, it will be much slower. For a good interactive experience, a modern dedicated GPU (like an NVIDIA RTX 3060 or better) with at least 8 GB of VRAM is recommended.
LangChain: The oldest and most comprehensive framework, offering extensive integrations but often criticized for its steep learning curve and boilerplate code.
LlamaIndex: Primarily focused on data-intensive applications, excelling at connecting language models to external data sources through advanced retrieval and indexing.
AutoGen (Microsoft): A multi-agent framework that shines at creating conversational agents that can collaborate and delegate tasks to solve complex problems.
CrewAI: Designed for orchestrating role-playing autonomous agents, making it easy to define agents with specific jobs and have them work together in a structured crew.
AgentVerse: A versatile framework that provides a “lego-like” approach to building and composing customized multi-agent environments for various applications.
ChatDev: A “virtual software company” framework where different agents (CEO, programmer, tester) simulate a software development lifecycle to complete coding tasks.
SuperAGI: A developer-centric framework focused on building autonomous agents with useful features like provisioning, deployment, and a graphical user interface.
AI Droid (by Vicuna): A lightweight and fast framework designed for mobile and edge devices, prioritizing efficiency and low-resource consumption.
GPTeam: Similar to ChatDev, this framework uses role-playing agents (like product managers and engineers) to collaboratively work on development tasks from a single prompt.
Agenta: An open-source platform that helps developers evaluate, test, and deploy language model applications with features for prompt management and A/B testing.
OpenAI Assistants API: OpenAI’s native solution for building stateful, assistant-like agents directly on their platform, handling conversation history and tool integration internally.
LangGraph: Built on LangChain, this framework is specifically for creating cyclical, stateful multi-agent workflows, treating agent interactions as steps in a graph.
