• Źródło i zakres raportu: Podsumowanie przygotowano na podstawie raportu płacowego firmy Sedlak & Sedlak („Puls Biznesu”), obejmującego dane z ponad 1300 przedsiębiorstw i ponad 408 tysięcy pracowników na 1264 stanowiskach.
• Wpływ awansu na zarobki: Rynek bardzo silnie premiuje unikalne kompetencje i odpowiedzialność strategiczną. Największy przyrost całkowitego wynagrodzenia (aż o 86%) odnotowuje się przy awansie ze stanowiska eksperta (SE) na starszego eksperta (SSE).
• Ogólne rozpiętości płacowe (UoP vs B2B):
  • Mediana całkowitego wynagrodzenia rośnie od 7 180 zł dla młodszego specjalisty do 25 700 zł dla starszego eksperta.
  • W przypadku kontraktów B2B stawki są wyższe i kształtują się w przedziale od 9 437 zł do 29 139 zł.
• Najlepiej opłacane stanowiska w IT i nowych technologiach (dla Starszych Ekspertów - SSE i Ekspertów - SE):
  • Architekt systemowy (SSE) – 29 000 zł
  • Specjalista ds. bezpieczeństwa IT (SSE) – 28 069 zł
  • Konsultant SAP (SSE) – 28 000 zł
  • Programista embedded (SSE) – 27 500 zł
  • Programista back-end (SE) – 27 129 zł
  • Inżynier cloud (SE) – 26 500 zł
  • Product Owner (SSE) – 25 750 zł
• Sektor poza IT: Najwyższymi zarobkami poza technologiami wyróżniają się prawnicy, gdzie np. radca prawny na poziomie starszego eksperta (SSE) osiąga zarobki rzędu 25 100 zł.
• Kluczowy wniosek: Wysokość wynagrodzenia na współczesnym rynku pracy zależy przede wszystkim od realnego wpływu na organizację i ponoszonej odpowiedzialności biznesowej, a nie od samego stażu pracy.

https://www.legacy.com/us/obituaries/northjersey/name/donald-bolander-obituary?id=24573183

Founded Career Institute and edited several of their books on English, spelling, synonyms, antonyms, etc.

大多数人认为获得诺贝尔奖的科学家会留在知名机构，但John Jumper选择离开DeepMind加入竞争中的Anthropic，这可能表明他对新公司的创新方向和潜力有更深的信心。

• Software engineering has a long history of aggressive automation—from assembly to high-level languages—and rather than replacing engineers, every leap in productivity has expanded the scale and complexity of what can be built.
• The demand for software is functionally insatiable; as soon as engineers become more efficient, the organizational goalposts move, leading to higher expectations rather than a reduction in staff.
• Current AI development tools act primarily as force multipliers rather than autonomous agents, meaning that an expert developer is still strictly required to drive, review, and handle the remaining high-value 10% of the work.
• For AI to truly replace software engineers, an autonomous AI system would need to consistently outperform an AI+human developer hybrid team, a milestone that current data and architectures are far from reaching.
• While generalist software engineers remain secure, specific narrow domains or commoditized skill sets (such as basic, boilerplate frontend development) face a heightened risk of being entirely absorbed by AI tools.
• The most significant hurdle for autonomous AI is not initial code generation, but rather the long-term maintenance, context retention, and reasoning required to safely adapt to changing ecosystems and walled gardens.
• Rather than destroying the engineering market, AI changes the underlying economics of production, allowing developers to rapidly clear backlogs, build minor utilities, and focus more on architectural architecture and system design.

Hacker News Discussion

• The Jevons Paradox of Code: Commenters emphasized that increasing the efficiency of software creation lowers its cost, which historically exponentially increases overall demand rather than exhausting the market.
• The Rise of Bespoke Consumer Software: A popular theory suggested that AI will enable everyday users to spin up personalized, ad-free, micro-utilities (like custom todo lists) on the fly, reducing reliance on bloated commercial applications.
• The Tinkering vs. Maintenance Chasm: Several users countered the "bespoke software" future by comparing it to 3D printing; while creating a custom script is easy with AI, the average user lacks the logical thinking and patience required to maintain software over time.
• A Cyberpunk Technological Stack: Users noted that the current trajectory feels reminiscent of science fiction, where individuals possess highly customized, personalized technology stacks modified specifically for their unique workflows.
• B2B Complexity and Standardization: Many participants pointed out that while consumer-facing apps might become fragmented, enterprise B2B infrastructure, distributed systems, and core data layers (like the Linux kernel or banking infrastructure) strictly require human-driven rigor, consistency, and standardization.

• Many software engineers should deliberately work fewer hours and at a slower pace, aiming for around 80% utilization by default to leave 20% slack time away from the computer.
• Engineering performance in tech companies is dominated by outlier, high-impact events where solving the right problem at the right time matters more than raw effort.
• Key time-dependent opportunities for outsized impact include stepping in to unblock a massive enterprise deal, mitigating or preventing a major incident early, and rapidly unblocking high-profile feature shipments.
• Staying 100% utilized on low-priority backlog tasks makes engineers too busy to spot high-impact opportunities, and prevents managers from volunteering them for strategic, high-visibility work.
• Keeping time free and "doing nothing" gives the brain rest to spark new ideas, prevents exhaustion before high-pressure incidents, and encourages engineers to "think in slow motion" during critical situations rather than making frantic, harmful changes.
• Engineers must consciously avoid low-priority "glue work" (like unsolicited documentation or unprioritized tech debt) because insulating an organization from its own poor prioritization leads to individual burnout without reward.
• Being overly helpful leaves engineers vulnerable to "predators" who extract uncompensated, unrecorded work through backchannels, such as product managers asking for ad-hoc data queries or colleagues taking credit for paired programming.
• Engineers should resist the urge to immediately implement volatile requirements from indecisive designers or run out the clock on low-clout managerial ideas that are likely to be canceled, avoiding wasted effort.
• Peak high performance does not require constant grinding; it is more effective to maintain an 80% effort baseline during ordinary times and save 100% maximum effort for the two or three times a year when the rewards are exceptionally high.

Hacker News Discussion

• The Firefighter Incentive Problem: Multiple commenters noted a fundamental misalignment in corporate game theory: preventing an outage yields zero visibility or measurable metrics, whereas creating "a giant pile of kindling" and publicly putting out the resulting fire gets rewarded twice by management.
• Strategies for Technical Relevance: Rather than relying on firefighting visibility, some users suggested building robust, highly reliable, yet complex essential tools that force other teams to repeatedly come back to you for guidance, naturally cementing your status as an expert.
• The Risk of Over-Helpfulness vs. Goodwill: While the author warned against backchannel "predators," a popular counterpoint detailed how a Principal Engineer achieved their title by intentionally giving away credit and building immense team goodwill, which paid off critically during a high-stakes project rescue.
• Product Search vs. Pure Engineering: There was nuance added around the balance of code quality; in early-stage feature exploration or "search problems," moving fast and breaking things to find out what users want can sometimes be more valuable than building perfectly solid, slow-moving architecture.

• The author is a senior software engineer with a decade of professional experience, primarily focused on web backend development, including settlement processing and accounting systems.
• Historically, the author took pride in their deep domain expertise, debugging intuition, and ability to meticulously design complex systems before writing a single line of code.
• The author experienced a profound realization when their manager noted that while their code delivery speed was excellent, they were taking too long to write up design documents.
• Upon testing a high-performing LLM, the author was shocked to find that the AI could rapidly structure and organize the most complex, specialized logic that had taken them years of sweat and tears to master.
• While human engineers are still fundamentally necessary to pilot the models, review the output, and act as the "human in the loop," the author feels their role has been reduced to that of an "off-the-shelf," replaceable commodity.
• The decline in the perceived value of high-quality, handcrafted technical design has led to a sense of existential dread, making the author feel as though they spent ten years building a skillset that is rapidly becoming obsolete.
• The author expresses profound uncertainty and sadness regarding the future of their software engineering career, feeling trapped in a shifting landscape where their hard-earned specialization no longer differentiates them from generalists.

Hacker News Discussion

• The Leveling of the Playing Field: Commenters validated the author's observation that AI acts as a massive equalizer. A single domain expert paired with an LLM can now scale their output, write documentation, and conduct reviews at a pace that replaces a larger team of specialized human experts, drastically reducing the market premium for deep technical specialization.
• Management Misconceptions and Operational Risk: A major point of discussion centered on the behavior of non-technical leadership. Many managers mistakenly believe LLMs can completely replace engineering talent, leading them to substitute experienced seniors with cheaper juniors to prompt the AI; users warned this will result in long-term architectural debt and severe system maintenance crises.
• The Looming Threat of White-Collar Homogenization: Several users noted that this existential crisis is not unique to software engineering. If software development is disrupted to this extent, almost all knowledge-based, white-collar professions are vulnerable to similar devaluation, shifting the ultimate value away from individual expertise and toward the owners of capital.
• Loss of Cognitive Critical Thinking: Participants argued that outsourcing the initial design phase to AI erodes foundational critical thinking. Because LLMs automatically gloss over omissions with confident assumptions, engineers stop asking hard, preventative architectural questions, shifting the discovery of critical edge cases to much later in the lifecycle.
• A Counter-Perspective on LLM Limitations: Conversely, some engineers disagreed with the author's anxiety, asserting that fields like finance, taxation, and complex distributed infrastructure remain heavily insulated. They noted that AI agents regularly make confidently flawed logic errors, meaning true accountability, edge-case mitigation, and systemic understanding still absolutely require human experts.

大多数人认为专业化和专业化各有价值且会长期共存，但作者认为市场正在迫使所有人成为通才，这与'专业化和专业化将长期共存'的职业发展主流认知相悖。

大多数人认为AI会替代人类工作或限制职业发展，但作者认为AI实际上能让人承担更雄心勃勃的项目，扩大职责范围并加速职业发展。这挑战了AI导致工作减少或职业停滞的常见担忧，表明AI可能是职业扩张的催化剂而非替代品。

大多数人认为计算机科学和编程技能仍然是就业的保证，但作者认为这些技能可能不再是工作的保证，这解释了计算机科学专业人数的下降。这一观点挑战了传统技术教育价值的认知，暗示AI正在改变就业市场的基本规则。

大多数人认为技术人才在AI时代会通过适应和学习而增值，作者认为许多软件工程师感到他们的核心技能正在贬值，导致职业前景不明和深度职业倦怠。

大多数人认为AI技术要么是创造机会的积极力量，要么是威胁就业的消极因素，但作者认为AI同时扮演着双重矛盾角色——既是少数人的财富彩票，又是多数人职业安全的威胁。

1. Treat managing up as a true partnership: Proactively adapt to and embrace collaboration with your manager rather than remaining passive or treating the relationship as a purely top-down power dynamic.
2. Lead with the punchline: Reverse traditional storytelling by stating your conclusion, headline, or recommendation first, ensuring busy executives know exactly why they should care right away.
3. Show your thinking process: Map out the logical steps and considerations behind your conclusions so your manager can easily follow and trust your rationale.
4. Flag potential risks early: Surface issues or roadblocks before they escalate into an immediate crisis, giving your manager time to help course-correct.
5. Bring solutions, not complaints: When presenting a problem, always pair it with actionable solutions or structured options rather than simply unloading the issue onto your manager.
6. Prioritize the information you share: Filter out noise and organize updates by importance to significantly reduce the cognitive load on your manager.
7. Keep your manager loop-aware: Provide consistent, predictable updates so your manager is never left in the dark or caught off-guard by external inquiries.
8. Differentiate micromanagement from under-communication: Evaluate whether a manager's constant hovering is actually due to your own lack of proactive communication and visibility.
9. Over-communication is often the baseline: What feels like "too much" communication to you is frequently just the right amount of context and security required by a busy leader.
10. Proactively suggest next steps: Take immediate ownership of your workflow by mapping out and presenting the logical follow-up actions yourself.
11. State assertions and opinions, don’t just ask questions: Move away from open-ended questions and instead state clear recommendations, backing them up with data or reasoned perspectives.
12. Anticipate your manager’s questions: Pre-emptively think through the pushback, blind spots, or clarifying details your manager will likely ask about, and address them upfront.
13. Adapt strictly to their preferred communication channels: Match your manager’s style—whether they prefer quick Slack updates, structured formal documents, or async deep dives.
14. Build trust before you actually need it: Consistently deliver on small commitments over time to establish a reliable track record, securing autonomy and leeway for future high-stakes initiatives.
15. Be direct and honest with feedback: When explicitly asked for your opinion or critique, respect the trust given to you by offering specific, actionable, and psychologically safe feedback.

作者认为AI编码工具对大多数普通人来说仍然难以掌握，它们是现有经验的放大器而非替代品，因此不担心自己的职业会被取代。

Summary of The 2026 Global Intelligence Crisis

• Current Economic Context (2026): The article describes a 2026 landscape where unemployment is at 4.28%, AI capital expenditure accounts for 2% of GDP ($650bn), and over 2,800 data centers are planned for construction in the U.S.
• The Diffusion Narrative: Contrary to fears of mass displacement, the author argues that the speed of AI adoption is following a traditional S-curve rather than an exponential explosion. Data shows that daily intensive use of AI for work remains stable rather than accelerating non-linearly.
• Economic Constraints on AI: Recursive technology (AI improving itself) does not equate to recursive economic adoption. Deployment is bounded by physical capital, energy costs, and the marginal cost of compute. If compute becomes more expensive than human labor, substitution will stop.
• Productivity as a Supply Shock: AI is framed as a positive supply shock that lowers costs and increases real income. History suggests that productivity surges expand the "consumption frontier" and create new industries rather than collapsing aggregate demand.
• Labor Market Resilience: Software engineering job postings are rising (up 11% YoY in the provided data), and construction hiring is surging due to data center demand.
• The Keynesian Parallel: Just as Keynes wrongly predicted a 15-hour work week in 1930, the author suggests humans will likely use AI gains to consume more and higher-quality services rather than withdrawing from the labor market.

Hacker News Discussion

• Skepticism Toward Statistics: Many commenters criticized the article for "lying with statistics." They pointed out that the 11% YoY rise in job postings uses a depressed scale on the Y-axis and a cherry-picked timeframe (late 2025 to early 2026) that ignores the massive crash from the 2022 hiring peak.
• The "Vibe" of the Writing: Users debated the authorship of the post, with some calling it "AI slop" or an exaggerated version of McKinsey-style consulting prose, though others noted typos that suggested human authorship.
• Impact of Tax Laws: Several participants attributed the 2022–2023 software job slump to Section 174 tax changes (requiring R&D amortization) rather than AI displacement, arguing that the recent "recovery" is just a normalization of those tax shocks.
• Complement vs. Substitute: A central theme in the comments was whether AI enables "vibe coding"—allowing fewer engineers to do more, or allowing non-technical staff to build tools—and whether this ultimately increases the total volume of software projects or reduces the headcount of professional engineers.
• Critique of Data Sources: There was a debate regarding the reliance on Indeed data, with some noting that while Indeed scrapes many sites, it may not accurately capture the hiring trends of elite tech startups that use specialized platforms like Greenhouse or Ashby.

• Human-Centric Engineering: Senior engineers emphasize that while AI excels at writing syntax, it cannot replicate the human ability to understand customer problems, business context, and the "why" behind a project.
• Mastery of Fundamentals: Staying relevant requires a deep understanding of core computer science principles (data structures, algorithms, system design), as these allow engineers to vet and debug the often-flawed code generated by LLMs.
• Strategic Tool Adoption: Rather than fearing AI, experienced developers view it as a sophisticated "power tool" or "junior pair programmer" that accelerates boilerplate tasks, allowing them to focus on high-level architecture.
• Emphasis on Soft Skills: Communication, empathy, and leadership are highlighted as "durable skills" that AI cannot automate; being able to bridge the gap between technical constraints and business goals is more valuable than ever.
• The "Judgment" Gap: AI models lack the foresight to predict long-term maintenance costs or technical debt; senior engineers are now increasingly acting as "editors" or "judges" of AI-generated solutions.
• Continuous Adaptability: The consensus is that the role of an engineer is shifting from "writing code" to "solving problems," requiring a mindset that is willing to pivot and learn new paradigms as the tech stack evolves.

• The "Invisible" Employee: Leyla Kazim describes a year-long experiment in a previous corporate role where she essentially stopped working to see if anyone would notice.
• Exploiting Managerial Gaps: Following a department restructure that left her with a vague role and a distracted manager, she realized her output wasn't being tracked or valued.
• Performative Productivity: To maintain the illusion of work, she used "visual cues" of busyness, such as keeping complex spreadsheets open and spending 15 minutes a week fabricating data for slide decks that she knew would never be audited.
• Time Reclaimed: Instead of performing job duties, she spent her office hours planning an extensive 10-month trip around the world, effectively using the company's time and salary to fund her personal exit strategy.
• Existential Dread: Despite the "success" of the ruse, Kazim reflects on the soul-crushing nature of "meaningless theatre," noting that trading one's life force for a paycheck in a redundant role is a form of personal tragedy.
• Final Resignation: She eventually resigned of her own accord, concluding that the experience proved "hard work" in a corporate setting is often more about perceived performance than actual utility.

Hacker News Discussion

• Moral and Personal Integrity: Many commenters argued that while "time theft" might feel like a victory against a cold corporation, it often leads to a loss of self-respect and professional skill atrophy.
• The "Bullshit Jobs" Reality: Discussion centered on David Graeber's theory of "Bullshit Jobs," with users sharing their own stories of being stuck in roles where their absence would have zero impact on the organization.
• Employer Resentment: Some users expressed frustration with the author, suggesting that this type of behavior justifies heavy-handed management, surveillance, and the replacement of human roles with AI.
• Corporate Failure vs. Individual Ethics: There was a debate on whether the blame lies with the individual for being "dishonest" or with the organization for being so dysfunctional and bloated that a full-time employee could disappear for a year unnoticed.
• The "Boreout" Phenomenon: Several participants noted that doing nothing is often more exhausting and mentally taxing than being busy, leading to a specific type of workplace depression known as "boreout."

• Soft Skills and Judgment: Commenters emphasize that empathy, social skills, and the ability to build relationships remain highly valuable, as AI cannot truly navigate corporate politics or seek mutual human benefit.
• Domain Expertise: While AI can generate code or content, humans are still required to provide the "judgment" to determine what is worth building and to foresee how architectural decisions will impact a project years down the line.
• Physical Trades: Many users suggest that "blue-collar" trades—such as plumbing, electrical work, and construction—are the most future-proof because the physical dexterity and adaptability required for these tasks are far beyond current robotic capabilities.
• Communication: Superior written and verbal communication is cited as a vital skill, both for leadership and for effectively "prompting" or directing AI tools to achieve specific professional goals.
• Critical Thinking: The ability to identify when a task definition is wrong or when a product doesn't "make sense" for a human user is seen as a distinct human advantage over models that follow instructions literally.
• Legal and Accountability Roles: Jobs that require a "human in the loop" for legal liability or ethical reasons—such as doctors, lawyers, and military personnel—are considered safer from complete automation.
• Metalearning: The most important skill may be the ability to learn new tools quickly and discard old ones without emotional attachment, adapting as the technology evolves.

这个观点强调了早期职业生涯对于工程师技能形成的重要性。

大多数人认为在裁员潮中，高额使用AI工具的员工可能会被视为成本负担而被裁掉，但作者提出了一个颠覆性的观点：像Jeremy这样大量使用AI工具的员工不仅没有被裁员，反而获得了更大的杠杆效应和影响力。这挑战了人们对AI成本与价值的传统认知。

大多数人认为UX设计师的核心价值在于通用用户体验设计，但作者认为这一角色在AI时代面临被取代的风险。这一挑战性观点暗示设计师需要向架构型或业务型方向发展，否则可能被AI和产品管理双重挤压，反映了行业对设计师角色未来发展的深刻思考。

这一观点挑战了软件开发行业的职业发展路径，暗示AI代理将从根本上改变程序员的技能需求和工作性质。从编码者到项目管理者的转变，代表着一种职业身份的颠覆性重构，这与人们对技术专业发展的传统认知形成鲜明对比。

• The article argues that answering the "What is your current/expected salary?" question with a single number is a strategic mistake that limits your earning potential.
• Giving a specific number early in the process creates an "anchor" that recruiters will use to keep the offer as low as possible.
• Instead of providing a number, the author suggests pivoting the conversation toward the value you bring to the role and the total compensation package.
• A key strategy is to ask for the company's budgeted range for the position first, which puts the onus on the employer to disclose their limits.
• If forced to give a range, ensure the bottom of your range is the minimum you would actually accept, while the top represents your "dream" scenario.
• The goal of salary discussions in early interviews should be to establish "alignment" rather than a final price tag.
• Delaying the specific salary talk until after you have "wowed" the team gives you more leverage, as they are now invested in hiring you specifically.

Hacker News Discussion

• Many commenters emphasize that while "not answering" is a common piece of advice, it can be impractical for those who lack extreme leverage or are in urgent need of work.
• A popular counter-strategy mentioned is to confirm the salary range during the very first recruiter call to avoid wasting hours on interviews for a role that cannot meet your financial requirements.
• Users suggested that if you do provide a number first, you should always include a disclaimer that you "look at the entire package holistically" (benefits, equity, PTO) to maintain flexibility for later negotiation.
• There is a consensus that once a final offer is made, you should almost always ask, "Is there any way you can come up a little bit from that?" as this simple question frequently results in a 5-10% bump with minimal risk.
• Some participants shared "pleasant surprise" stories where refusing to name a price led to offers significantly higher (+50% or more) than what they would have asked for.
• The discussion highlights a shift toward transparency, with many noting that asking for the "salary band" is becoming a standard and respected practice in tech hiring.

this is a great model for approaching new environments -- it allows for one to focus explicitly on identifying the discourse community and integrating ourselves into that discourse community to make the transition more smooth.

This would especially be most valuable for those that are neurodivergent, especially Autism and ADHD, etc.

This is good advice -- not just for individual courses that we take from different Discourse Communities, but as we define our majors and focuses, it is important to join the discourse so that we can stay abreast of the latest information, discussions and practical solutions within it.

Student specific - "You are encouraged to develop the capabilities relevant to your role, recognizing that this is a continuous process."

Suggest replacing with course from LinkedIn Learning

Student specific - is there a staff alternative to this blog post?

• Finding Wins Above Replacement: Look for candidates who made a significant impact in their past roles, distinguishing their actions from team efforts. This highlights their talent, agency, and individuality.

• Key Traits to Identify:

  • Creativity: Ability to identify bigger problems and opportunities rather than merely following orders.
  • Resourcefulness & Follow-through: Persistence to complete tasks even when facing obstacles.
  • Vision: A thoughtful, original future vision with coherent life choices.

• Effective Questions to Ask:

  • "Tell me about your best [accomplishment/task]."
  • "If your life was a book, give me the chapter titles from birth till now."

• Chip on the Shoulder: Candidates with a drive to prove themselves can be valuable. Look for those with optimism and resilience rather than deep insecurities or negativity.

• Challenges as Motivation: Present company challenges and assess if candidates are excited by the opportunity to make an impact rather than deterred by imperfection.

• High EQ & Persuasion: Evaluate their ability to adapt communication and influence effectively, especially in group interactions.

• Theory for Excellence: Past excellence in any field (sports, arts, academics) suggests transferable drive and discipline for unrelated roles.

• Understanding Competence: Candidates should know their strengths, weaknesses, and preferred tools or methods.

• Spike Potential: Tailor questions to their profile and assess specific strengths that hint at future excellence.

• Coachability & Openness: Look for candidates open to feedback, willing to improve, and capable of committing even when they disagree.

• Unpretentiousness: Seek candidates who are self-aware, lighthearted, and pleasant to work with.

• Assessment Methods:

  • Use scenario-based interviews, group exercises, or role-playing to evaluate real-time reactions.
  • Gather backchannel references for additional insights.

This is such a clever way to create a container that otherwise might not have existed for that work. I wonder if this would be a good way to highlight glue work?

origine

I listen to this video when I need inspiration and confidence. It reminds me of what is important, that we can change the world, and how I would like it to change.

Should use this in concert with the Commitment Inventory exercise.

They went through 310 role descriptions and, even though role descriptions may vary significantly, they found 3 core skills that a large percentage of MLOps roles required:

📦 Docker and Kubernetes 🐍 Python 🌥 Cloud

always end positively.

Event examples to be around your major skills.

Example typewritten jokes from Joan Rivers' card index of comedy:

The #1 sign of a successful career

2-3 months may turn into 1 year

Progress is anything that moves you closer to your goal, it doesn't have to be a huge breakthrough, it doesn't take heroic effort, even a small step can go a long way.

One moment this article describes Eno as eschewing conventional jobs, but then describes him going back to two different ones. The second one as an electronics dealer is at least tangential to his music/sound career and may have helped give him some tools for operating in the space which he wanted to be.

“I hung on because…”

"Nullum numen abest si sit prudentia, is unquestionably true, with regard to every thing except poetry; and I am very sure that any man of common understanding may, by proper culture, care, attention, and labour, make himself whatever he pleases, except a good poet." Chesterfield, Letter lxxxi.

See also: https://en.m.wikisource.org/wiki/Page:The_Works_of_the_Rev._Jonathan_Swift,_Volume_5.djvu/261 as a source

Swift, Jonathan. The Works of the Rev. Jonathan Swift. Edited by Thomas Sheridan and John Nichols. Vol. 5. 19 vols. London: H. Baldwin and Son, 1801.

Advantage of U-shaped hybrid

Advantage of T-shaped hybrid

U-shaped hybrid type of specialization

T-shaped hybrid type of specialization

Job stages

(see a list of activities below this annotation)

That imagination makes one to ponder for a pretty good time :)

Your career goal that is assign from your major.

Similar to the way that bi-lingual/dual immersion language students may temporarily fall behind their peers in 3rd and 4th grade, but rocket ahead later in high school, those interested in interdisciplinary work may seem to lag, but later outpace their lesser specializing peers.

What is the underlying mechanism for providing the acceleration boosts in these models? Are they really the same or is this effect just a coincidence?

Is there something about the dual stock and double experience or even diversity of thought that provides the acceleration? Is there anything in the pedagogy or productivity research space to explain it?

1. "Tell me about the last time you refactored a module or class."
2. "Tell me about the last piece of user feedback that became a feature."
3. "Tell me about the last feature of yours that got dropped."

"The need to engage with people in terms of evaluating them for the aim of acquiring a different point of view was one occasion this semester where the knowledge I received in class positively changed the way I approached an issue. I was patient enough to explore other perspectives, some of which disagreed with mine, so that I might learn about their opinions without bias or prejudice."

Asshole test: 60 minutes, 8 people, no right answer, but checking who turns into an asshole under pressure

https://threadreaderapp.com/thread/1518214731026292736.html

Some useful truths here.

Learnings: - Take a lesson from good hill climbing algorithms, and drop yourself in unfamiliar situations to find your career maximum. - Progress in artificial games (e.g. career ladders) is fun, but you're likely to miss the bigger picture.

Meet atleast any one Core objectives for each student

... If I was asked in a teaching staff meeting, about my role as a teacher in a semester, I will say, fulfilling atleast any one core objective in the subject curriculum to each and every student is my highest priority *if this has been the priority for every teacher and the pressurising management during covid online classes, the [[covid batch wisdom catastrophe]] would not be this much impact - I can certainly write an feature article on this covid batch catastrophe topic - especially who passed 10th as covid batch, during start of the covid now directly face 12th public face-off with [[post-covid zero knowledge calibre]]. And then going to join college without learning subject fundamentals [[Devastating post-covid college education for freshers]]

What engineers mostly want from job offers

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• Challenging voice: Communicating in a way that challenges the status quo and is focused on new ideas and efficiency.
• Supportive voice: Communicating in a way that strengthens social ties and trust, and builds friendly cohesion of a team.

COVID is disrupting scientific careers around the world. (2021). Nature, 599(7884), 179–179. https://doi.org/10.1038/d41586-021-03049-6

Way to nicely explain why you have left your previous job

Understanding COVID-19 Registration Form. (n.d.). Google Docs. Retrieved March 31, 2021, from https://docs.google.com/forms/d/e/1FAIpQLScrC4krKVHcSm_Wi2VcXcGHa4F8l3cilL2xhbB62etcK4sy4w/viewform?usp=embed_facebook

How real world problems differ from the school ones

Engineer's market value over time:

valuable HR tip

3M State of Science Index | How People Feel About Science (US). (n.d.). Retrieved 18 June 2021, from https://www.3m.com/3M/en_US/state-of-science-index-survey/

"always be quitting" = making yourself "replaceable"

23 social skills to look for in great engineers

Related to the idea of career capital, which is the set of knowledge and skills that makes you hard to replace.

You must first generate this capital by becoming good at something rare and valuable. It is something that makes you hard to replace and is therefore the result of putting effort into developing skills that differentiate you from others.

Newport, C. (2016). Deep Work: Rules for Focused Success in a Distracted World (1 edition). Grand Central Publishing.

List of advantages for working 4 days per week (instead of 5)

Leeming, J. (2020). Careers and coronavirus: Sign up for expert advice straight to your inbox. Nature. https://doi.org/10.1038/d41586-020-01837-0

Ro, C. (2020). Pandemic harms Canadian grad students’ research and mental health. Nature. https://doi.org/10.1038/d41586-020-02441-y

Prime, H., Wade, M., May, S., Jenkins, J., & Browne, D. (2021). The COVID-19 Family Stressor Scale: Validation and Measurement Invariance in Female and Male Caregivers. PsyArXiv. https://doi.org/10.31234/osf.io/7328w

• Trap: "I just need to work on myself more"
• Assumption: You need to focus on improving X
• Impact: You need to consider the environment. if you don't, you could end up in a place that limits your ability to work on that skill instead of amplify it.

If impact is the input that powers career progression, what are the complicating factors and why?

• do we switch projects?
• teams?
• career paths?
• what projects do we take on, vs not, and why?

Many reasons why Data Engineer is a great starting position for junior developers

Who the industry really needs

Why Data Engineering became more important

Takeaway from the analysis:

• ~70% more open data engineer than data scientist positions
• ~40% more open ML engineer than data scientist positions
• only ~30% as many ML scientist as data scientist positions

4 different data profiles (and more): consolidated:

How data was collected

The resulting 70% is based on a real analysis

What tasks can Data Scientist be responsible for

Structure of a BERT engineer job

staffplus