Here is this week's digest:

Ask HN: How does one stay motivated to grind through LeetCode?

Many professionals struggle with LeetCode motivation, especially when it feels disconnected from real-world work. Key strategies to overcome this include:

• Prioritize Discipline over Motivation: Rely on routine, consistent planning, and breaking down tasks into manageable chunks (e.g., 15-minute sessions, 1 hard/2 medium/3 easy problems daily).
• Shift Perspective: View it as a puzzle for enjoyment, a learning opportunity for underlying algorithms/data structures, or a competitive challenge. Gamified platforms like Neetcode can help.
• Set Clear Goals & Identify Patterns: Don't aimlessly grind; understand common problem 'buckets' and algorithms. Resources like Blind 75 lists can provide structure.
• Acknowledge the 'Why': Recognize that LeetCode often acts as a gatekeeper for high-paying big tech roles, particularly in the Bay Area. Understanding this reality, even if frustrating, can provide extrinsic motivation (e.g., higher salary, family support).
• Consider Alternatives: If the grind is too much, explore roles or regions less reliant on LeetCode, network aggressively, or focus on personal projects. Some suggest the rise of AI may shift interview paradigms.

Ultimately, it's about finding a sustainable approach, whether by embracing the challenge, accepting its necessity, or seeking different paths.

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Ask HN: Anyone else disillusioned with "AI experts" in their team?

Many individuals are experiencing disillusionment with "AI experts" in their teams, observing a significant gap in fundamental understanding of AI and LLMs.

Key observations and insights include:

• Competence Gap: A widespread issue where "AI experts" lack basic knowledge of how language models work, what "AI" truly means (often confusing it with just LLMs or machine learning), or even where "self-hosted" models are actually running (often third-party APIs).
• Hype vs. Substance: The current boom attracts career climbers and those prioritizing buzzwords over deep expertise, leading to a focus on marketing over technical depth.
• Misrepresentation Risks: Claiming "self-hosted" models while using external services like OpenAI or Anthropic poses serious compliance and legal risks.
• Stochastic Nature: Discussion around LLM non-determinism, distinguishing between intentional sampling (like top-k) and unintentional factors like floating-point non-associativity in computations.
• Navigating the Landscape: It's crucial to validate your own understanding, be aware of the industry's hype cycle, and understand that truly deep AI expertise (especially in building models) is rare. For those facing this, focusing on practical learning and strategic career planning is advised, as these issues are not unique to AI.

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Ask HN: Is building for the web even worth it now?

Many long-time internet users are feeling disengaged from the web due to an influx of AI-generated content, bots, and algorithmic social media feeds. This leads to a perception of declining authenticity and difficulty finding genuine human interaction.

However, the discussion highlights that the "good internet" is still accessible, but requires a more deliberate approach. Key strategies include:

• Step away from algorithmic feeds: Consciously disengage from platforms that exploit attention with AI-generated content and excessive noise.
• Seek out niche communities: Discover genuine human interaction through personal blogs, RSS feeds, Mastodon instances, and specialized forums.
• Curate your online experience: Utilize tools like blogrolls and small web directories (e.g., ooh.directory, blogroll.org, Kagi's Small Web) and consider self-hosting services to build a personalized, less noisy "sub-internet."
• Embrace independent platforms: Support and participate in spaces dedicated to human-made content and authentic connection, creating what you wish to see online.

The sentiment is that while the signal-to-noise ratio is shifting, valuable human content and communities persist for those willing to actively seek them out and foster alternatives.

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Ask HN: Is Computer Science still a good choice?

The value of a Computer Science degree is increasingly tied to genuine interest and deep skill development. While the tech job market faces a cyclical downturn and heightened competition, AI is largely seen as an augmentation tool rather than a full replacement for creative engineers. Success now demands strong problem-solving, system design, and adaptable engineering thinking, alongside developed job hunting skills.

Key takeaways:

• Passion is crucial: Genuine interest helps navigate a competitive market.
• Market is cyclical: Current challenges are likely temporary; an upswing is anticipated.
• AI augments, not replaces: Focus on complex tasks that AI struggles with (ambiguity, moonshots).
• Upskill deeply: Master problem-solving, system design, and performance beyond basic coding.
• Job hunting is a skill: Cultivate networking and 'cultural fit' abilities.
• Consider niches: Specialized STEM fields, healthcare, or trades offer alternative stability.

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Ask HN: Cloud providers are losing in favor of bare-metal?

The prevailing sentiment is that major cloud providers are not broadly losing ground based on financial growth, but a "right-sizing" trend is emerging. Many organizations are re-evaluating cloud usage, especially for stable workloads or smaller projects where cost is paramount.

• Key takeaways: * Cloud giants continue strong year-over-year growth, indicating no widespread decline. * The shift is towards "right-sizing" technology estates; using cloud as a tool, not a default. * When bare metal/dedicated servers make sense: For solo indie developers, small teams, or companies with stable, non-scaling workloads prioritizing cost control and full infrastructure ownership. Providers like Hetzner or the combination of Netcup + Cloudron + Backblaze are popular alternatives. * Handling bare metal infrastructure: * DB Backups: Implement replicas and upload dumps to S3-compatible object storage. * CI/CD: Utilize tools such as GitLab and Argo CD. * Logging: Deploy an ELK stack or use SSH and journalctl. * When cloud excels: For huge corporations avoiding hardware overhead, early-stage startups needing speed and elasticity (especially with unpredictable growth), and businesses with fluctuating usage profiles requiring rapid scaling. * Startup credits are still available but with stricter filters due to past abuse.

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