GenAI Isn’t Replacing Devs (And It Shouldn’t)
💌 Forget the awe of of GenAI, here’s the “all too human”Welcome to HackerPulse Dispatch! In this edition, we’re exploring some of the hottest topics in tech, from why replacing programmers with AI mi
Welcome to HackerPulse Dispatch! In this edition, we’re exploring some of the hottest topics in tech, from why replacing programmers with AI might just be the biggest mistake you can make, to the fascinating journey behind the greatest calculator app development story.
We’ll also discuss the upcoming performance boost in Python 3.14 and share insights from the Anthropic Research Fellowship interview.
Here’s what new:
📱 “A Calculator App? Anyone Could Make That.”: Android’s calculator isn’t just accurate—it’s a feat of engineering that solved the fundamental limitations of floating-point math.
🔥 Tech’s Dumbest Mistake: Why Firing Programmers for AI Will Destroy Everything: Tech companies betting on AI to replace programmers will soon face a talent crisis, broken systems, and skyrocketing costs to rehire the very engineers they let go.
👾 No, Your GenAI Model Isn’t Going to Replace Me: A seasoned contractor identified critical performance bottlenecks missed by automated tests, proving that intuition, expertise, and hands-on problem-solving remain irreplaceable in software engineering.
🙌 I Failed My Anthropic Interview and Came to Tell You All About It So You Don’t Have To: Anthropic’s research fellowship interview process pushes candidates to demonstrate coding skills, creativity, and cultural fit through a series of high-pressure challenges, from automated coding tests to brainstorming sessions.
💨 A New Interpreter in Python 3.14 Delivers a Free Speed Boost: Python 3.14 introduces a new interpreter that enhances performance by up to 30% with no modifications needed to existing code or C extension modules.
“A Calculator App? Anyone Could Make That.”(🔗 Read Paper)
Making a calculator that consistently delivers precise answers is a monumental challenge. The iOS calculator famously fails simple expressions like (10100)+1−(10100)(10^{100}) + 1 - (10^{100})(10100)+1−(10100), while Android gets it right—thanks to Hans-J. Boehm, the legendary engineer behind Google's garbage collection efforts.
Tasked with building the perfect calculator, Boehm had to navigate floating-point precision errors, algebraic arithmetic, and even the limits of real number representation.
What followed was an engineering odyssey involving bignums, constructive real arithmetic, and a groundbreaking hybrid approach that balanced speed with correctness.
Key Points
The floating-point problem: Standard floating-point numbers can’t represent values like 1010010^{100}10100 or even simple fractions like 0.3, making precise calculations impossible. To fix this, Boehm’s team started using bignums—arbitrarily large integers—but that only solved part of the problem.
Going deeper with real numbers: The team explored algebraic numbers (like 2\sqrt{2}2) and eventually constructive real arithmetic (RRA). This allowed Android’s calculator to handle expressions involving π\piπ and other irrationals correctly.
The final breakthrough: Instead of relying entirely on RRA, which struggles with equality checks, Boehm’s team combined rational arithmetic with symbolic representations of key values like π\piπ and 2\sqrt{2}2.
Tech’s Dumbest Mistake: Why Firing Programmers for AI Will Destroy Everything (🔗 Read Paper)
The tech industry has a short memory. Not long ago, programmers were revered as architects of the digital world. Now, they’re seen as outdated and expensive, with AI positioned as the ultimate replacement.
But as companies rush to cut engineering teams, history warns that this strategy is a ticking time bomb. The fallout will be brutal: undertrained junior devs, chaotic AI-generated code, and a hiring crisis for skilled engineers.
Key Points
A lost generation of programmers: By sidelining junior developers, companies are creating a talent vacuum. Without real-world problem-solving experience, the next wave of engineers will be little more than AI prompt jockeys.
AI can’t fix its own mess: Companies laying off engineers will soon realize AI-generated code is a liability. Security flaws, maintenance nightmares, and compliance disasters will send them scrambling to rehire the very programmers they dismissed.
Elite programmers will be pricier than ever: As skilled engineers become rarer, demand will drive salaries sky-high. The few who truly understand systems will name their price, and desperate companies will have no choice but to pay up.
No, Your GenAI Model Isn’t Going to Replace Me (🔗 Read Paper)
In the past three weeks, a seasoned contractor identified and resolved critical performance issues that potentially saved the company hundreds of thousands of dollars.
Despite having no bonuses to show for it, the satisfaction of solving complex technical problems was its own reward. His deep-rooted experience and intuition led him to suspect a system bottleneck, even though all standard tests passed without issue.
Through rigorous analysis with VisualVM, custom test scenarios, and precise debugging, he pinpointed two unsuspecting lines of code among thousands. The result? A staggering 10x increase in throughput, boosting developer productivity and earning well-deserved recognition.
Key Points
Beyond standard testing: The performance issues weren’t traditional bugs; all tests passed. However, experience-driven intuition and deep system profiling uncovered inefficiencies that traditional code reviews missed.
The limits of AI in software development: Despite advancements in GenAI, it lacks the human intuition, creativity, and problem-solving skills honed through years of hands-on experience. The belief that AI can replace mid-level engineers entirely by 2025 raises concerns about the future of software development.
A call to young developers: While AI tools may generate code, true expertise comes from learning, thinking, and writing code independently. The best developers leverage their humanity, intuition, and creativity—traits that no machine can replicate.
I Failed My Anthropic Interview & Came to Tell You All About It So You Don’t Have To (🔗 Read Paper)
Anthropic, the company behind Claude, is creating waves with its powerful AI models and a research fellowship program that stands out in the tech world.
With backing from Amazon and a focus on safety, their selection process is rigorous and designed to assess more than just coding skills. The interview process is split into multiple stages, each testing different aspects of a candidate’s abilities, from coding to creativity to cultural fit.
Key Points
Stage 1: Online Coding (1.5 hours): Candidates are required to build a class with a public API under tight time constraints, emphasizing speed over optimization. The challenge is impersonal and automated, demanding raw coding execution over fancy algorithms.
Stage 2: Face-to-Face Coding (1 hour): A LeetCode-style problem is presented with a twist, testing problem-solving ability in a more traditional format. Preparation through typical coding platforms like LeetCode is recommended.
Stage 3: Virtual Onsite (Three Parts): This stage includes a research brainstorm, a take-home assignment, and a culture fit session. The most unique part is the research brainstorm, where candidates must generate creative ideas on the spot, a challenging task that tests adaptability and creativity under pressure.
A New Interpreter in Python 3.14 Delivers a Free Speed Boost (🔗 Read Paper)
Python 3.14, set for release later this year, is introducing a major update to its interpreter that could boost performance by up to 30%, all while ensuring complete compatibility with existing code.
This improvement stems from a new optimization based on tail calls, a concept previously unfamiliar to most Python developers. Crucially, this change will require no modifications to Python code or C extension modules, making it a seamless performance upgrade for many.
However, devs building from source will need to enable the feature manually, and certain C compilers are required for support.
Key Points
Tail call-based interpreter: The new interpreter, leveraging tail call optimization at the C compiler level, boosts performance without altering existing code.
No changes needed for compatibility: Python code and C extension modules don’t need to be updated, reducing maintenance overhead for developers.
Compilation requirements: For those building from source, the feature requires a C compiler that supports tail call optimization, such as MSVC or Clang 19+.
🎬 And that's a wrap! Stay tuned for your weekly dose of tech news and insights.