Week of December 27, 2025

This Week's Top Videos

Intel Shares Fall on Reports Nvidia to Halt Chipmaking Tests | Bloomberg Tech 12/24/2025

By Bloomberg Technology

The 'boring' AI infrastructure stocks like memory chip makers tripled in 2025 while flashy AI applications struggled to monetize. Intel's foundry dreams took a hit as Nvidia halted production tests, highlighting the brutal competition in chip manufacturing. The shift shows investors are rewarding picks-and-shovels players over software trying to build moats.

• Memory chip makers like Micron roughly tripled in stock price while traditional AI darlings like Adobe and Salesforce struggled, showing infrastructure beats applications
• Nvidia halted production tests at Intel's foundries, dealing a blow to Intel's turnaround story despite $5M investment and 10% government stake
• AI software companies failed to monetize their AI tools effectively, with many facing margin pressure from new competing models entering the market
• Storage companies like Western Digital and SanDisk doubled/tripled as AI infrastructure demand created unexpected winners in 'boring' hardware sectors
• Market expects rotation from pure tech to non-tech sectors that can boost productivity with AI tools, as capex spending faces ROI scrutiny
• Microsoft, Amazon, and Google positioned best for AI overspending concerns since they can rent out capacity and turn investments into immediate revenue

The Genius Strategy Everyone Gets Wrong

By 20VC

Stop trying to fix your weaknesses—you'll only move from 'idiot' to 'mediocre' at best. Instead, double down on your genius-level skills and build teams that complement your weak spots. With effort, you can only move up 1-2 buckets on the competency histogram, so focus where you're already strong.

• Map all your tasks to a competency histogram with buckets from 'idiot' to 'genius'—this mental model reveals where you should focus your energy
• You can only move up 1-2 competency buckets maximum with great effort, so improving weaknesses caps you at mediocre performance
• Time spent improving weaknesses is time NOT spent on genius-level activities where you create exponential value
• Build complementary teams instead of trying to be well-rounded—it's easier to find people who cover your weak areas
• The biggest mistake high-performers make is focusing on their worst skills instead of maximizing their natural strengths

Netflix Co-CEO on Netflix's strategy to expand into games, live, sports, etc

By Acquired

Netflix's Co-CEO reveals the entertainment ecosystem is splitting into two poles: hyper-personalized niche content and massive shared live experiences where millions watch simultaneously. The shift from 'we'll never do live/sports' to embracing traditional formats only works at scale when you become the default entertainment destination, not just a disruptor.

• Early-stage companies should focus on counterpositioning against incumbents by highlighting what they don't have, but at scale you can layer on traditional approaches to compete directly
• The entertainment ecosystem is polarizing into two distinct value propositions: highly personalized niche experiences versus shared live experiences with millions of simultaneous viewers
• On-demand was Netflix's original differentiator against traditional TV, but strategic positioning must evolve as you reach scale and become the primary destination rather than an alternative
• Live content creates unique value through shared experiences where millions know they're doing the same thing simultaneously, enabling collective conversation and connection
• When you become the place people show up to be entertained rather than a disruptor, you can start competing with traditional formats you previously avoided

Our favorite open source projects of 2025

By GitHub

GitHub's 2025 picks reveal a surprising trend: simple, founder-bootstrapped tools are outperforming VC-backed solutions. Just a Job App has tracked 3,000 applications with zero funding by solving the abandoned spreadsheet problem through automated email parsing. For builders, this shows product-market fit beats funding when you solve real daily frustrations.

• Job application tracking succeeds when it eliminates manual work—Just a Job App auto-updates from confirmation emails, avoiding the spreadsheet abandonment problem that plagues job seekers
• Simple blog generators can capture developer mindshare by targeting the specific pain of devs who say they'll create blogs but never do—Marmite focuses on this exact use case
• Open-source hardware projects gain momentum alongside product launches—Summer Cart 64 N64 flashcard benefits from Analog 3D console shipping after 2-year wait
• Bootstrapped solutions can achieve significant traction without VC funding—Just a Job App tracked 3,000 applications proving market validation through usage metrics alone
• Hardware projects succeed when both hardware designs and firmware are fully open-source, enabling DIY builds and commercial pre-built options on platforms like AliExpress

Developer Experience in the Age of AI Coding Agents – Max Kanat-Alexander, Capital One

By ai.engineer

AI coding agents fail spectacularly on legacy codebases, often writing tests that just confirm "button pushed successfully" rather than actual validation. The key isn't just adopting agents—it's investing in industry-standard dev tools, CLI/API interfaces, and testable code architecture because agents can't fight the training set any better than humans can debug untestable systems.

• Use industry-standard tools in standard ways because that's what's in the AI training set—fighting the training set with custom package managers or obscure languages dramatically reduces agent effectiveness
• Agents need CLI or API interfaces to take action effectively—while computer use exists, text-based interaction is their most native and accurate format
• High-quality validation with clear error messages dramatically increases agent capabilities—agents can't interpret '500 internal error' any better than humans can
• Agents write useless tests on untestable codebases, creating tests that just confirm basic operations rather than meaningful validation
• Well-structured codebases enable agents to reason about code directly rather than relying on iterative trial-and-error, dramatically improving their effectiveness
• Legacy enterprise codebases without proper testing infrastructure force both humans and agents into ineffective debugging cycles instead of productive development