Archive for the ‘en-US’ Category
[GoogleIO2024] Developer Keynote: Innovations in AI and Development Tools at Google I/O 2024
The Developer Keynote at Google I/O 2024 showcased a transformative vision for software creation, emphasizing how generative artificial intelligence is reshaping the landscape for creators worldwide. Delivered by a team of Google experts, the session highlighted accessible AI models, enhanced productivity across platforms, and new tools designed to simplify complex workflows. This presentation underscored Google’s commitment to empowering millions of developers through an ecosystem that spans billions of devices, fostering innovation without the burden of underlying infrastructure challenges.
Advancing AI Accessibility and Model Integration
A core theme of the keynote revolved around making advanced AI capabilities available to every programmer. The speakers introduced Gemini 1.5 Flash, a lightweight yet powerful model optimized for speed and cost-effectiveness, now accessible globally via the Gemini API in Google AI Studio. This tool balances quality, efficiency, and affordability, enabling developers to experiment with multimodal applications that incorporate audio, video, and extensive context windows. For instance, Jacqueline demonstrated a personal workflow where voice memos and prior blog posts were synthesized into a draft article, illustrating how large context windows—up to two million tokens—unlock novel interactions while reducing computational expenses through features like context caching.
This approach extends beyond simple API calls, as the team emphasized techniques such as model tuning and system instructions to personalize outputs. Real-world examples included Loc.AI’s use of Gemini for renaming elements in frontend designs from Figma, enhancing code readability by interpreting nondescript labels. Similarly, Invision leverages the model’s speed for real-time environmental descriptions aiding low-vision users, while Zapier automates podcast editing by removing filler words from audio uploads. These cases highlight how Gemini empowers practical transformations, from efficiency gains to user delight, encouraging participation in the Gemini API developer competition for innovative applications.
Enhancing Mobile Development with Android and Gemini
Shifting focus to mobile ecosystems, the keynote delved into Android’s evolution as an AI-centric operating system. With over three billion devices, Android now integrates Gemini to enable on-device experiences that prioritize privacy and low latency. Gemini Nano, the most efficient model for edge computing, powers features like smart replies in messaging without data leaving the device, available on select hardware like the Pixel 8 Pro and Samsung Galaxy S24 series, with broader rollout planned.
Early adopters such as Patreon and Grammarly showcased its potential: Patreon for summarizing community chats, and Grammarly for intelligent suggestions. Maru elaborated on Kotlin Multiplatform support in Jetpack libraries, allowing shared business logic across Android, iOS, and web, as seen in Google Docs migrations. Compose advancements, including performance boosts and adaptive layouts, were highlighted, with examples from SoundCloud demonstrating faster UI development and cross-form-factor compatibility. Testing improvements, like Android Device Streaming via Firebase and resizable emulators, ensure robust validation for diverse hardware.
Jamal illustrated Gemini’s role in Android Studio, evolving from Studio Bot to provide code optimizations, translations, and multimodal inputs for rapid prototyping. A demo converted a wireframe image into functional Jetpack Compose code, underscoring how AI accelerates from ideation to implementation.
Revolutionizing Web and Cross-Platform Experiences
The web’s potential was amplified through AI integrations, marking its 35th anniversary with tools like WebGPU and WebAssembly for on-device inference. John discussed how these enable efficient model execution across devices, with examples like Bilibili’s 30% session duration increase via MediaPipe’s image recognition. Chrome’s enhancements, including AI-powered dev tools for error explanations and code suggestions, streamline debugging, as shown in a Boba tea app troubleshooting CORS issues.
Aaron introduced Project IDX, now in public beta, as an integrated workspace for full-stack, multiplatform development, incorporating Google Maps, DevTools, and soon Checks for privacy compliance. Flutter’s updates, including WebAssembly support for up to 2x performance gains, were exemplified by Bricket’s cross-platform expansion. Firebase’s evolution, with Data Connect for SQL integration, App Hosting for scalable web apps, and Genkit for seamless AI workflows, further simplifies backend connections.
Customizing AI Models and Future Prospects
Shabani and Lawrence explored open models like Gemma, with new variants such as PaliGemma for vision-language tasks and the upcoming Gemma 2 for enhanced performance on optimized hardware. A demo in Colab illustrated fine-tuning Gemma for personalized book recommendations, using synthetic data from Gemini and on-device inference via MediaPipe. Project Gameface’s Android expansion demonstrated accessibility advancements, while an early data science agent concept showcased multi-step reasoning with long context.
The keynote concluded with resources like accelerators and the Google Developer Program, emphasizing community-driven innovation. Eugene AI’s emissions reduction via DeepMind research exemplified real-world impact, reinforcing Google’s ecosystem for reaching global audiences.
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[DotJs2024] Generative UI: Bring your React Components to AI Today!
The fusion of artificial intelligence and frontend development is reshaping how we conceive interactive experiences, placing JavaScript engineers at the vanguard of this transformation. Malte Ubl, CTO at Vercel, captivated audiences at dotJS 2024 with a compelling exploration of Generative UI, a pivotal advancement in Vercel’s AI SDK. Originally hailing from Germany and now entrenched in Silicon Valley’s innovation hub, Ubl reflected on the serendipitous echoes of past tech eras—from CGI uploads via FTP to his own contributions like Whiz at Google—before pivoting to AI’s seismic impact. His message was unequivocal: frontend expertise isn’t obsolete in the AI surge; it’s indispensable, empowering developers to craft dynamic, context-aware interfaces that transcend textual exchanges.
Ubl framed the narrative around a paradigm shift from Software 1.0’s laborious machine learning to Software 2.0’s accessible, API-driven intelligence. Where once PhD-level Python tinkering dominated, today’s landscape favors TypeScript applications invoking large language models (LLMs) as services. Models have ballooned in scale and savvy, rendering fine-tuning optional and prompting paramount. This velocity—shipping products in days rather than years—democratizes AI development, yet disrupts traditional roles. Ubl’s optimism stems from a clear positioning: frontend developers as architects of human-AI symbiosis, leveraging React components to ground abstract prompts in tangible interactions.
Central to his demonstration was a conversational airline booking interface, where users query seat changes via natural language. Conventional AI might bombard with options like 14C or 19D, overwhelming without context. Generative UI elevates this: the LLM invokes React server components as functions, streaming a interactive seat map pre-highlighting viable window seats. Users manipulate the UI directly—selecting, visualizing availability—bypassing verbose back-and-forth. Ubl showcased the underlying simplicity: a standard React project with TypeScript files for boarding passes and seat maps, hot-module-reloading enabled, running locally. The magic unfolds via AI functions—React server components that embed client-side state, synced back to the LLM through an “AI state” mechanism. Selecting 19C triggers a callback: “User selected seat 19C,” enabling seamless continuations like checkout flows yielding digital boarding passes.
This isn’t mere novelty; Ubl underscored practical ramifications. End-user chatbots gain depth, support teams wield company-specific components for real-time adjustments, and search engines like the open-source Wary (a Perplexity analog) integrate existing product renderers for enriched results. Accessibility leaps forward too: retrofitting legacy sites with AI state turns static pages into voice-navigable experiences, empowering non-traditional input modalities. Ubl likened AI to a potent backend—API calls fetching not raw data, but rendered intelligence—amplifying human-computer dialogue beyond text. As models from OpenAI, Google Gemini, Anthropic’s Claude, and Mistral proliferate, frontend differentiation via intuitive UIs becomes the competitive edge, uplifting the stack’s user-facing stratum.
Ubl’s closing exhortation: embrace this disruption by viewing React components as AI-native building blocks. Vercel’s AI SDK examples offer starter chatbots primed for customization, accelerating prototyping. In a world where AI smarts escalate, frontend artisans—adept at state orchestration and visual storytelling—emerge as the revolution’s fulcrum, forging empathetic, efficient digital realms.
The Dawn of AI-Infused Interfaces
Ubl vividly contrasted archaic AI chats with generative prowess, using an airline scenario to highlight contextual rendering’s superiority. Prompts yield not lists, but explorable maps—streamed via server components—where selections feed back into the AI loop. This bidirectional flow, powered by AI state, ensures coherence, transforming passive queries into collaborative sessions. Ubl’s live demo, from flight selection to boarding pass issuance, revealed the unobtrusive elegance: plain React, no arcane setups, just LLM-orchestrated functions bridging intent and action.
Empowering Developers in the AI Era
Beyond demos, Ubl advocated for strategic adoption, spotlighting use cases like e-commerce search enhancements and accessibility overlays. Existing components slot into AI workflows effortlessly, while diverse models foster toolkit pluralism. The SDK’s documentation and examples lower barriers, inviting experimentation. Ubl’s thesis: as AI commoditizes logic, frontend’s artistry—crafting modality-agnostic interactions—secures its primacy, heralding an inclusive future where developers orchestrate intelligence with familiar tools.
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[DevoxxFR 2024] Debugging Your Salary: Winning Strategies for Successful Negotiation
At Devoxx France 2024, Shirley Almosni Chiche, an independent IT recruiter and career agent, delivered a dynamic session titled “Debuggez votre salaire ! Mes stratégies gagnantes pour réussir sa négociation salariale.” With over a decade of recruitment experience, Shirley unpacked the complexities of salary negotiation, offering actionable strategies to overcome common obstacles. Through humor, personas, and real-world insights, she empowered developers to approach salary discussions with confidence and preparation, transforming a daunting process into a strategic opportunity.
Navigating the Salary Minefield
Shirley opened with a candid acknowledgment: salary discussions are fraught with tension, myths, and frustrations. Drawing from her role at Build RH, her recruitment firm, she likened salary negotiation to a high-stakes race, where candidates endure lengthy recruitment processes only to face disappointing offers. Common employer excuses—“we must follow the salary grid,” “we can’t pay more than existing staff,” or “the budget is tight”—often derail negotiations, leaving candidates feeling undervalued.
To frame her approach, Shirley introduced six “bugs” that justify low salaries, each paired with a persona representing typical employer archetypes. These included the rigid “Big Corp” manager enforcing salary grids, the team-focused “Didier Deschamps” avoiding pay disparities, and the budget-conscious “François Damiens” citing financial constraints. Other personas, like the overly technical “Elon” scrutinizing code, the relentless negotiator “Patrick,” and the discriminatory “Hubert,” highlighted diverse challenges candidates face.
Shirley shared market insights, noting a 2023–2024 tech slowdown with 200,000 global layoffs, reduced venture funding, and a shift toward cost-conscious industries like banking and retail. This context, she argued, demands strategic preparation to secure fair compensation.
Countering the Bugs: Tactical Responses
For each bug, Shirley offered counter-arguments rooted in empathy and alignment with employer priorities. Against the salary grid, she advised exploring non-salary benefits like profit-sharing or PERCO plans, common in large firms. Using a “mirror empathy” tactic, candidates can frame salary needs in the employer’s language—e.g., linking pay to productivity. Challenging outdated grids by highlighting market research or internal surveys also strengthens arguments.
For the “Didier Deschamps” persona, Shirley suggested emphasizing unique skills (e.g., full-stack expertise in a backend-heavy team) to justify higher pay without disrupting team cohesion. Proposing contributions like speaking at conferences or aiding recruitment can further demonstrate value. She shared a success story where a candidate engaged the team directly, securing a better offer through collective dialogue.
When facing “François Damiens” and financial constraints, Shirley recommended focusing on risk mitigation. For startups, candidates can negotiate stock options or bonuses, arguing that their expertise accelerates product delivery, saving recruitment costs. Highlighting polyvalence—combining skills like development, data, and security—positions candidates as multi-role assets, justifying premium pay.
For technical critiques from “Elon,” Shirley urged immediate feedback post-interview to address perceived weaknesses. If gaps exist, candidates should negotiate training opportunities to ensure long-term fit. Pointing out evaluation mismatches (e.g., testing frontend skills for a backend role) can redirect discussions to relevant strengths.
Against “Patrick,” the negotiator, Shirley advised setting firm boundaries—two rounds of negotiation max—to avoid endless haggling. Highlighting project flaws tactfully and aligning expertise with business goals can shift the dynamic from adversarial to collaborative.
Addressing Discrimination: A Sobering Reality
Shirley tackled the “Hubert” persona, representing discriminatory practices, with nuance. Beyond gender pay gaps, she highlighted biases against older candidates, neurodivergent individuals, those with disabilities, and career switchers. Citing her mother’s experience as a Maghrebi woman facing a 20% pay cut, Shirley acknowledged the harsh realities for marginalized groups.
Rather than dismissing discriminatory offers outright, she advised viewing them as career stepping stones. Candidates can leverage such roles for training or experience, using “mirror empathy” to negotiate non-salary benefits like remote work or learning opportunities. While acknowledging privilege, Shirley urged resilience, encouraging candidates to “lend an ear to learning” and rebound from setbacks.
Mastering Preparation: Anticipating the Negotiation
Shirley emphasized proactive preparation as the cornerstone of successful negotiation. Understanding one’s relationship with money—shaped by upbringing, traumas, or social pressures—is critical. Some candidates undervalue themselves due to impostor syndrome, while others see salary as a status symbol or family lifeline. Recognizing these drivers informs negotiation strategies.
She outlined key preparation steps:
- Job Selection: Target roles within your expertise and in high-paying sectors (e.g., cloud, security) for better leverage. Data roles can yield 7–13% salary gains.
- Market Research: Use resources like Choose Your Boss or APEC barometers to benchmark salaries. Shirley noted Île-de-France salaries exceed regional ones by 10–15K, with a 70K ceiling for seniors in 2023.
- Company Analysis: Assess financial health via LinkedIn or job ad longevity. Long-posted roles signal negotiation flexibility.
- Recruiter Engagement: Treat initial recruiter calls as data-gathering opportunities, probing team culture, hiring urgency, and technical expectations.
- Value Proposition: Highlight impact—product roadmaps, technical migrations, or team mentoring—early in interviews to set a premium tone.
Shirley cautioned against oversharing personal financial details (e.g., current salary or expenses) during salary discussions. Instead, provide a specific range (e.g., “around 72K”) based on market data and role demands. Mentioning parallel offers tactfully can spur employers to act swiftly.
Sealing the Deal: Confidence and Coherence
In the final negotiation phase, Shirley advised a 48-hour reflection period after receiving an offer, consulting trusted peers for perspective. Counteroffers should be fact-based, reiterating interview insights and using empathetic language. Timing matters—avoid Mondays or late Fridays for discussions.
Citing APEC data, Shirley noted that 80% of executives who negotiate are satisfied, with 65% securing their target salary or higher. She urged candidates to remain consistent, avoiding last-minute demands that erode trust. Beyond salary, consider workplace culture, inclusion, and work-life balance to ensure long-term fit.
Shirley closed with a rallying call: don’t undervalue your skills or settle for less. By blending preparation, empathy, and resilience, candidates can debug their salary negotiations and secure rewarding outcomes.
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Hashtags: #SalaryNegotiation #DevoxxFrance #CareerDevelopment #TechRecruitment
[PHPForumParis2023] You Build It, You Run It: Observability for Developers – Smaïne Milianni
Smaïne Milianni, a former taxi driver turned PHP developer, delivered an engaging talk at Forum PHP 2023, exploring the “You Build It, You Run It” philosophy and the critical role of observability in modern development. Now an Engineering Manager at Yousign, Smaïne shared insights from his decade-long journey in PHP, emphasizing how observability tools like logs, metrics, traces, and alerts empower developers to maintain robust applications. His practical approach and humorous delivery offered actionable strategies for PHP developers to enhance system reliability and foster a culture of continuous improvement.
The Essence of Observability
Smaïne introduced observability as the cornerstone of the “You Build It, You Run It” model, where developers are responsible for both building and maintaining their applications. He explained how observability encompasses logs, metrics, traces, and custom alerts to monitor system health. Using real-world examples, Smaïne illustrated how these tools help identify issues, such as application errors or system outages, before they escalate. His emphasis on proactive monitoring resonated with developers seeking to ensure their PHP applications remain stable and performant.
Implementing Observability in PHP
Diving into practical applications, Smaïne outlined how to integrate observability into PHP projects. He highlighted tools like Datadog for collecting metrics and traces, and demonstrated how to set up alerts for critical incidents, such as P1 outages that trigger SMS and email notifications. Smaïne stressed the importance of prioritizing alerts based on severity to avoid notification fatigue. His examples, drawn from his experience at Yousign, provided a clear roadmap for developers to implement observability, ensuring rapid issue detection and resolution.
The Power of Post-Mortems
Smaïne concluded by emphasizing the role of post-mortems in fostering a virtuous cycle of improvement. Responding to an audience question, he explained how his team conducts weekly manager reviews to track post-mortem actions, ensuring they are prioritized and addressed. By treating errors as learning opportunities rather than failures, Smaïne’s approach encourages developers to refine their code and systems iteratively. His talk inspired attendees to adopt observability practices that enhance both technical reliability and team collaboration.
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[PyConUS 2024] How Python Harnesses Rust through PyO3
David Hewitt, a key contributor to the PyO3 library, delivered a comprehensive session at PyConUS 2024, unraveling the mechanics of integrating Rust with Python. As a Python developer for over a decade and a lead maintainer of PyO3, David provided a detailed exploration of how Rust’s power enhances Python’s ecosystem, focusing on PyO3’s role in bridging the two languages. His talk traced the journey of a Python function call to Rust code, offering insights into performance, security, and concurrency, while remaining accessible to those unfamiliar with Rust.
Why Rust in Python?
David began by outlining the motivations for combining Rust with Python, emphasizing Rust’s reliability, performance, and security. Unlike Python, where exceptions can arise unexpectedly, Rust’s structured error handling via pattern matching ensures predictable behavior, reducing debugging challenges. Performance-wise, Rust’s compiled nature offers significant speedups, as seen in libraries like Pydantic, Polars, and Ruff. David highlighted Rust’s security advantages, noting its memory safety features prevent common vulnerabilities found in C or C++, making it a preferred choice for companies like Microsoft and Google. Additionally, Rust’s concurrency model avoids data races, aligning well with Python’s evolving threading capabilities, such as sub-interpreters and free-threading in Python 3.13.
PyO3: Bridging Python and Rust
Central to David’s talk was PyO3, a Rust library that facilitates seamless integration with Python. PyO3 allows developers to write Rust code that runs within a Python program or vice versa, using procedural macros to generate Python-compatible modules. David explained how tools like Maturin and setup-tools-rust simplify project setup, enabling developers to compile Rust code into native libraries that Python imports like standard modules. He emphasized PyO3’s goal of maintaining a low barrier to entry, with comprehensive documentation and a developer guide to assist Python programmers venturing into Rust, ensuring a smooth transition across languages.
Tracing a Function Call
David took the audience on a technical journey, tracing a Python function call through PyO3 to Rust code. Using a simple word-counting function as an example, he showed how a Rust implementation, marked with PyO3’s @pyfunction attribute, mirrors Python’s structure while offering performance gains of 2–4x. He dissected the Python interpreter’s bytecode, revealing how the CALL instruction invokes PyObject_Vectorcall, which resolves to a Rust function pointer via PyO3’s generated code. This “trampoline” handles critical safety measures, such as preventing Rust panics from crashing the Python interpreter and managing the Global Interpreter Lock (GIL) for safe concurrency. David’s step-by-step breakdown clarified how arguments are passed and converted, ensuring seamless execution.
Future of Rust in Python’s Ecosystem
Concluding, David reflected on Rust’s growing adoption in Python, citing over 350 projects monthly uploading Rust code to PyPI, with downloads exceeding 3 billion annually. He predicted that Rust could rival C/C++ in the Python ecosystem within 2–4 years, driven by its reliability and performance. Addressing concurrency, David discussed how PyO3 could adapt to Python’s sub-interpreters and free-threading, potentially enforcing immutability to simplify multithreaded interactions. His vision for PyO3 is to enhance Python’s strengths without replacing it, fostering a symbiotic relationship that empowers developers to leverage Rust’s precision where needed.
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Hashtags: #Rust #PyO3 #Python #Performance #Security #PyConUS2024 #DavidHewitt #Pydantic #Polars #Ruff
[DevoxxUK2024] Productivity is Messing Around and Having Fun by Trisha Gee & Holly Cummins
In their DevoxxUK2024 talk, Trisha Gee (Gradle) and Holly Cummins (Red Hat, Quarkus) explore developer productivity through the lens of joy and play, challenging conventional metrics like lines of code. They argue that developer satisfaction drives business success, drawing on Fred Brooks’ The Mythical Man-Month to highlight why programmers enjoy crafting, solving puzzles, and learning. However, they note that developers spend only ~32% of their time coding, with the rest consumed by toil (e.g., waiting for builds, context-switching).
The speakers critique metrics like lines of code, citing examples where incentivizing code volume led to bloated, unmaintainable codebases (e.g., ASCII art comments). They warn against AI tools like Copilot generating verbose, unnecessary code (e.g., redundant getters/setters in Quarkus), which increases technical debt. Instead, they advocate for frameworks like Quarkus that reduce boilerplate through build-time bytecode inspection, enabling concise, expressive code.
Trisha and Holly introduce the SPACE framework (Satisfaction, Performance, Activity, Communication, Efficiency) as a holistic approach to measuring productivity, emphasizing developer well-being and flow over raw output. They highlight the importance of mental space for creativity, citing the brain’s default mode network, activated during low-stimulation activities like showering, running, or knitting. They encourage embracing “boredom” and play, supported by research showing happier developers are more productive. The talk critiques flawed metrics (e.g., McKinsey’s) and warns against management misconceptions, like assuming developers are replaceable by AI.
[DevoxxFR 2024] Staff Engineer: A Vital Role in Technical Leadership
My former estimated colleague François Nollen, a technical expert at SNCF Connect & Tech, delivered an engaging talk at Devoxx France 2024 on the role of the Staff Engineer. Often overshadowed by the more familiar Engineering Manager position, the Staff Engineer role is gaining traction as a critical path for technical leadership without management responsibilities. François shared his journey and insights into how Staff Engineers operate at SNCF Connect, offering a blueprint for developers aspiring to influence organizations at scale. This post explores the role’s responsibilities, its impact, and its relevance in modern tech organizations.
Defining the Staff Engineer Role
The Staff Engineer role, rooted in Silicon Valley’s tech giants, represents a senior technical contributor who drives impact across multiple teams without managing them directly. François described Staff Engineers as versatile problem-solvers, blending deep technical expertise with strong collaboration skills. Unlike Engineering Managers, who focus on team management, Staff Engineers tackle complex technical challenges, set standards, and foster innovation. At SNCF Connect, they are called “Technical Expertise Referents,” reflecting their role in guiding technical strategy and mentoring teams.
A Day in the Life
Staff Engineers at SNCF Connect enjoy significant autonomy, with no fixed daily tasks. François outlined a typical day, which begins with monitoring communication channels like Slack to identify team challenges. They contribute code, conduct reviews, and drive strategic initiatives, such as defining best practices or evaluating technical risks. Unlike team-bound developers, Staff Engineers operate at an organizational level, collaborating with engineering, HR, and communication teams to align technical and business goals. This broad scope requires a balance of technical depth and interpersonal finesse.
Impact and Collaboration
The influence of a Staff Engineer stems from their expertise and ability to inspire trust, not formal authority. François highlighted their role in unblocking teams, accelerating projects, and shaping technical strategy alongside Principal Engineers. At SNCF Connect, Staff Engineers work as a collective, amplifying their impact on cross-cutting initiatives like DevOps and continuous delivery. This collaborative approach contrasts with traditional roles like architects, who may be disconnected from delivery, making Staff Engineers integral to dynamic, agile environments.
Is It Right for You?
François posed a reflective question: is the Staff Engineer role suited for everyone? It demands extensive technical experience, organizational awareness, and strong communication skills. Developers who thrive on solving complex problems, mentoring others, and driving systemic change without managing teams may find this path rewarding. For organizations, Staff Engineers offer a framework to retain and empower experienced developers, avoiding the pitfalls of promoting them into unsuitable management roles, as per the Peter Principle.
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Hashtags: #StaffEngineer #TechnicalLeadership #DevoxxFrance #FrançoisNollen #SNCFConnect #Engineering #Agile
[DevoxxFR 2024] Going AOT: Mastering GraalVM for Java Applications
Alina Yurenko 🇺🇦 , a developer advocate at Oracle Labs, captivated audiences at Devoxx France 2024 with her deep dive into GraalVM’s ahead-of-time (AOT) compilation for Java applications. With a passion for open-source and community engagement, Alina explored how GraalVM’s Native Image transforms Java applications into compact, high-performance native executables, ideal for cloud environments. Through demos and practical guidance, she addressed building, testing, and optimizing GraalVM applications, debunking myths and showcasing its potential. This post unpacks Alina’s insights, offering a roadmap for adopting GraalVM in production.
GraalVM and Native Image Fundamentals
Alina introduced GraalVM as both a high-performance JDK and a platform for AOT compilation via Native Image. Unlike traditional JVMs, GraalVM allows developers to run Java applications conventionally or compile them into standalone native executables that don’t require a JVM at runtime. This dual capability, built on over a decade of research at Oracle Labs, offers Java’s developer productivity alongside native performance benefits like faster startup and lower resource usage. Native Image, GA since 2019, analyzes an application’s bytecode at build time, identifying reachable code and dependencies to produce a compact executable, eliminating unused code and pre-populating the heap for instant startup.
The closed-world assumption underpins this process: all application behavior must be known at build time, unlike the JVM’s dynamic runtime optimizations. This enables aggressive optimizations but requires careful handling of dynamic features like reflection. Alina demonstrated this with a Spring Boot application, which started in 1.3 seconds on GraalVM’s JVM but just 47 milliseconds as a native executable, highlighting its suitability for serverless and microservices where startup speed is critical.
Benefits Beyond Startup Speed
While fast startup is a hallmark of Native Image, Alina emphasized its broader advantages, especially for long-running applications. By shifting compilation, class loading, and optimization to build time, Native Image reduces runtime CPU and memory usage, offering predictable performance without the JVM’s warm-up phase. A Spring Pet Clinic benchmark showed Native Image matching or slightly surpassing the JVM’s C2 compiler in peak throughput, a testament to two years of optimization efforts. For memory-constrained environments, Native Image excels, delivering up to 2–3x higher throughput per memory unit at heap sizes of 512MB to 1GB, as seen in throughput density charts.
Security is another benefit. By excluding unused code, Native Image reduces the attack surface, and dynamic features like reflection require explicit allow-lists, enhancing control. Alina also noted compatibility with modern Java frameworks like Spring Boot, Micronaut, and Quarkus, which integrate Native Image support, and a community-maintained list of compatible libraries on the GraalVM website, ensuring broad ecosystem support.
Building and Testing GraalVM Applications
Alina provided a practical guide for building and testing GraalVM applications. Using a Spring Boot demo, she showcased the Native Maven plugin, which streamlines compilation. The build process, while resource-intensive for large applications, typically stays within 2GB of memory for smaller apps, making it viable on CI/CD systems like GitHub Actions. She recommended developing and testing on the JVM, compiling to Native Image only when adding dependencies or in CI/CD pipelines, to balance efficiency and validation.
Dynamic features like reflection pose challenges, but Alina outlined solutions: predictable reflection works out-of-the-box, while complex cases may require JSON configuration files, often provided by frameworks or libraries like H2. A centralized GitHub repository hosts configs for popular libraries, and a tracing agent can generate configs automatically by running the app on the JVM. Testing support is robust, with JUnit and framework-specific tools like Micronaut’s test resources enabling integration tests in Native mode, often leveraging Testcontainers.
Optimizing and Future Directions
To achieve peak performance, Alina recommended profile-guided optimizations (PGO), where an instrumented executable collects runtime profiles to inform a final build, combining AOT’s predictability with JVM-like insights. A built-in ML model predicts profiles for simpler scenarios, offering 6–8% performance gains. Other optimizations include using the G1 garbage collector, enabling machine-specific flags, or building static images for minimal container sizes with distroless images.
Looking ahead, Alina highlighted two ambitious GraalVM projects: Layered Native Images, which pre-compile base images (e.g., JDK or Spring) to reduce build times and resource usage, and GraalOS, a platform for deploying native images without containers, eliminating container overhead. Demos of a LangChain for Java app and a GitHub crawler using Java 22 features showcased GraalVM’s versatility, running seamlessly as native executables. Alina’s session underscored GraalVM’s transformative potential, urging developers to explore its capabilities for modern Java applications.
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Hashtags: #GraalVM #NativeImage #Java #AOT #AlinaYurenko #DevoxxFR2024
[DotAI2024] DotAI 2024: Stanislas Polu – Tracing the Evolution of LLM Reasoning and Agency
Stanislas Polu, a trailblazing researcher and co-founder of Dust, offered a panoramic view of large language models’ ascent at DotAI 2024. With a background spanning Polytechnique, Stanford, and pivotal roles at Stripe and OpenAI—where he advanced mathematical reasoning in LLMs—Polu now steers Dust toward AI-augmented enterprise tools. His discourse framed the AI epoch as a societal phase shift, paralleling seismic transitions like agriculture or electrification, and dissected how LLMs’ cognitive prowess is reshaping work and innovation.
Societal Shifts Catalyzed by Emergent Intelligence
Polu likened the pre- to post-AI era to historical ruptures, pinpointing AlphaZero’s 2017 debut as the inflection. This system, ingesting mere rules to master Go and chess beyond human bounds, evoked extraterrestrial ingenuity—crunching simulations to forge strategies unattainable through rote play. ChatGPT’s 2022 emergence amplified this, birthing agents that orchestrate tasks autonomously, while recent milestones like an AI securing a bronze at the International Mathematical Olympiad signal prowess in abstract deduction.
These strides, Polu observed, provoke institutional ripples: Nobel nods to AI-driven physics and biology breakthroughs affirm computation’s ascendancy in discovery. Yet, deployment lags potential; in mid-2022, OpenAI’s revenues hovered in tens of millions, with scant workplace adoption. This chasm propelled Polu’s pivot from research to product, hypothesizing that interfaces, not algorithms, bottleneck utility.
Dust embodies this thesis, granting teams bespoke assistants attuned to proprietary data and actions. Unlike monolithic bots, specialized agents—narrowly scoped for tasks like query resolution or report synthesis—yield superior accuracy by mitigating retrieval noise and model hallucinations. Polu’s narrative stresses infrastructure’s role: plumbing data silos and action endpoints to empower models without exposing sensitivities.
Unlocking Workplace Transformation Through Tailored AI
At Dust’s core lies dual convictions: seamless enterprise integration and multiplicity of agents. The former demands robust pipes—secure data federation and API orchestration—while the latter champions modularity, where assistants evolve via iterative refinement, drawing from domain lore to eclipse generalists.
Polu recounted Dust’s genesis amid GPT’s hype, yet workplace AI remains nascent, mired in “pre-GPT” paradigms of siloed tools. His solution: hyper-focused agents that ingest contextual artifacts, execute workflows, and iterate on feedback loops. This architecture not only boosts efficacy but fosters emergent behaviors, like chaining assistants for complex pipelines.
Envision a sales team querying leads enriched by CRM insights, or engineers debugging via code-aware bots—scenarios where Dust’s agnosticism across models ensures longevity. Polu advocated starting small: automate a 30-minute drudgery with GPT or Dust, scaling from there. This pragmatic ethos, he contended, unlocks boundless augmentation, where AI amplifies human ingenuity rather than supplants it.
As enterprises grapple with AI’s dual-edged sword—efficiency gains versus integration hurdles—Polu’s blueprint charts a collaborative path. Dust’s trajectory, blending research rigor with product agility, heralds a workspace where intelligence permeates, propelling productivity into uncharted realms.
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[DevoxxFR 2024] Super Tech’Rex World: The Assembler Strikes Back
Nicolas Grohmann, a developer at Sopra Steria, took attendees on a nostalgic journey through low-level programming with his talk, “Super Tech’Rex World: The Assembler Strikes Back.” Over five years, Nicolas modified Super Mario World, a 1990 Super Nintendo Entertainment System (SNES) game coded in assembler, transforming it into a custom adventure featuring a dinosaur named T-Rex. Through live coding and engaging storytelling, he demystified assembler, revealing its principles and practical applications. His session illuminated the inner workings of 1990s consoles while showcasing assembler’s relevance to modern computing.
A Retro Quest Begins
Nicolas opened with a personal anecdote, recounting how his project began in 2018, before Sopra Steria’s Tech Me Up community formed in 2021. He described this period as the “Stone Age” of his journey, marked by trial and error. His goal was to hack Super Mario World, a beloved SNES title, replacing Mario with T-Rex, coins with pixels (a Sopra Steria internal currency), and mushrooms with certifications that boost strength. Enemies became “pirates,” symbolizing digital adversaries.
To set the stage, Nicolas showcased the SNES, a 1990s console with a CPU, ROM, and RAM—components familiar to modern developers. He launched an emulator to demonstrate Super Mario World, highlighting its mechanics: jumping, collecting items, and battling enemies. A modified ROM revealed his custom version, where T-Rex navigated a reimagined world. This demo captivated the audience, blending nostalgia with technical ambition.
For the first two years, Nicolas relied on community tools to tweak graphics and levels, such as replacing Mario’s sprite with T-Rex. However, as a developer, he yearned to contribute original code, prompting him to learn assembler. This shift marked the “Age of Discoveries,” where he tackled the language’s core concepts: machine code, registers, and memory addressing.
Decoding Assembler’s Foundations
Nicolas introduced assembler’s essentials, starting with machine code, the binary language of 0s and 1s that CPUs understand. Grouped into 8-bit bytes (octets), a SNES ROM comprises 1–4 megabytes of such code. He clarified binary and hexadecimal systems, noting that hexadecimal (0–9, A–F) compacts binary for readability. For example, 15 in decimal is 1111 in binary and 0F in hexadecimal, while 255 (all 1s in a byte) is FF.
Next, he explored registers, small memory locations within the CPU, akin to global variables. The accumulator, a key register, stores a single octet for operations, while the program counter tracks the next instruction’s address. These registers enable precise control over a program’s execution.
Memory addressing, Nicolas’s favorite concept, likens SNES memory to a city. Each octet resides in a “house” (address 00–FF), within a “street” (page 00–FF), in a “neighborhood” (bank 00–FF). This structure yields 16 megabytes of addressable memory. Addressing modes—long (full address), absolute (bank preset), and direct page (bank and page preset)—optimize code efficiency. Direct page, limited to 256 addresses, is ideal for game variables, streamlining operations.
Assembler, Nicolas clarified, isn’t a single language but a family of instruction sets tailored to CPU types. Opcodes, mnemonic instructions like LDA (load accumulator) and STA (store accumulator), translate to machine code (e.g., LDA becomes A5 for direct page). These opcodes, combined with addressing modes, form the backbone of assembler programming.
Live Coding: Empowering T-Rex
Nicolas transitioned to live coding, demonstrating assembler’s practical application. His goal: make T-Rex invincible and alter gameplay to challenge pirates. Using Super Mario World’s memory map, a community-curated resource, he targeted address 7E0019, which tracks the player’s state (0 for small, 1 for large). By writing LDA #$01 (load 1) and STA $19 (store to 7E0019), he ensured T-Rex remained large, immune to damage. The # denotes an immediate value, distinguishing it from an address.
To nerf T-Rex’s jump, Nicolas manipulated controller inputs at addresses 7E0015 and 7E0016, which store button states as bitmasks (e.g., the leftmost bit for button B, used for jumping). Using LDA $15 and AND #$7F (bitwise AND with 01111111), he cleared the B button’s bit, disabling jumps while preserving other controls. He applied this to both addresses, ensuring consistency.
To restore button B for firing projectiles, Nicolas used 7E0016, which flags buttons pressed in a single frame. With LDA $16, AND #$80 (isolating B’s bit), and BEQ (branch if zero to skip firing), he ensured projectiles spawned only on B’s press. A JSL (jump to subroutine long) invoked a community routine to spawn a custom sprite—a projectile that moves right and destroys enemies.
These demos showcased assembler’s precision, leveraging memory maps and opcodes to reshape gameplay. Nicolas’s iterative approach—testing, tweaking, and re-running—mirrored real-world debugging.
Mastering the Craft: Hooks and the Stack
Reflecting on 2021, the “Modern Age,” Nicolas shared how he mastered code insertion. Since modifying Super Mario World’s original ROM risks corruption, he used hooks—redirects to free memory spaces. A tool inserts custom code at an address like $A00, replacing a segment (e.g., four octets) with a JSL (jump subroutine long) to a hook. The hook preserves original code, jumps to the custom code via JML (jump long), and returns with RTL (return long), seamlessly integrating modifications.
The stack, a RAM region for temporary data, proved crucial. Managed by a stack pointer register, it supports opcodes like PHA (push accumulator) and PLA (pull accumulator). JSL pushes the return address before jumping, and RTL pops it, ensuring correct returns. This mechanism enabled complex routines without disrupting the game’s flow.
Nicolas introduced index registers X and Y, which support opcodes like LDX and STX. Indexed addressing (e.g., LDA $00,X) adds X’s value to an address, enabling dynamic memory access. For example, setting X to 2 and using LDA $00,X accesses address $02.
Conquering the Game and Beyond
In a final demo, Nicolas teleported T-Rex to the game’s credits by checking sprite states. Address 7E14C8 and the next 11 addresses track 12 sprite slots (0 for empty). Using X as a counter, he looped through LDA $14C8,X, branching with BNE (branch if not zero) if a sprite exists, or decrementing X with DEX and looping with BPL (branch if positive). If all slots are empty, a JSR (jump subroutine) triggers the credits, ending the game.
Nicolas concluded with reflections on his five-year journey, likening assembler to a steep but rewarding climb. His game, nearing release on the Super Mario World hacking community’s site, features space battles and a 3D boss, pushing SNES limits. He urged developers to embrace challenging learning paths, emphasizing that persistence yields profound satisfaction.
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