The strongest professionals are not necessarily the best programmers. They are the people who can connect technology, business needs, teammates, and AI tools in one clear conversation.
Want to practice your listening skills? - listen to this
Are you a visual learner? - watch this
- AI & Tech
AI Coding Adoption 2026: 50 Statistics From 7 Surveys
Around 90% of developers now use AI coding tools [are you one of the 10%?], but trust in AI output has dropped to 29% — a serious gap between adoption and confidence in results.
Developers who use AI tools daily complete roughly 60% more pull requests per week than those who do not.
Copy-paste rates in codebases have doubled since 2021, and nearly half of AI-generated code samples fail security tests.
This report takes information from seven major developer surveys and gives a data-backed picture of what's actually happening in developer workflows.
AI tools speed up your daily work, but reviewers and senior engineers are noticing more copied and security-weak code. Using AI isn’t enough — you need to show you can judge and verify what it produces.
*Next time you use an AI coding tool, walk through the output line by line before committing it. Make sure you can explain what each part does in plain English. This one habit will protect your professional reputation as AI use becomes the standard.
- Jobs
Tech Hiring in 2026: AI Adoption, Talent Expectations and a Market in Rebalance.
The 2026 Tech Hiring Community Conference brought together recruiters and engineers from SAP, Microsoft, Zendesk, and Eventbrite, etc. to discuss what is changing in hiring — AI is now actively used throughout the recruitment process (interview summarisation, CV screening, salary benchmarking).
Rather than banning AI tools in technical interviews, many companies now let candidates use AI — and then evaluate whether the candidate understands and can explain the output.
Employer branding no longer starts at the job posting — it is built continuously through public presence in developer communities, open source contributions, and honest communication. Candidates evaluate companies long before the first call, and companies that ignore this are losing talent before the process begins.
If you have a technical interview coming up, being able to explain, question, and improve AI output, matters more than just reaching the correct answer.
*Practice explaining AI-generated code out loud. Use Claude or ChatGPT to generate a small solution to a problem, then record yourself explaining what the code does and why. If you struggle to explain it clearly, that’s what you need to work on.
- Skills & Upskilling
10 Key AI Workforce Trends In 2026
US job postings requiring AI skills grew 144% year-over-year as of April 2026, according to the Bipartisan Policy Center's AI Skills Dashboard — while overall job postings grew just 7%. The Stanford HAI 2026 AI Index found that AI-related skills now appear in 2.5% of all US job postings, a 297% increase over the past decade.
PwC's 2025 Global AI Jobs Barometer found that job numbers are rising even in highly automatable roles when workers actively use AI — confirming that reskilling around AI tools has a measurable career payoff, not just a theoretical one.
Organisational factors — company culture, management support, and governance structures — account for more than twice the variance in AI impact compared to individual technical skill or mindset alone. This means that understanding how to work effectively in a team, communicate with managers, and follow organisational processes matters as much as technical skill.
Growing your AI skills matters, but so does communicating how you use them. Developers who can explain what tools they use, how they verify output, and where they apply judgment alongside AI are more valuable to employers than developers who simply adopt more tools.
*Pick one AI tool you use at work. Write three sentences in English: (1) what it does, (2) when you use it, and (3) what you check before trusting its output. Practice saying these out loud — this is exactly what interviewers and team leads now ask for.
- Workplace & Communication
Why Human-Centric Skills Are the Ultimate Competitive Edge in the Age of AI
AI tools have lost their competitive advantage because most major companies now use them — to stand out you must be able to communicate across cultures, read difficult situations, and build human trust. Things that automated tools cannot replicate.
Deloitte's 2026 Global Human Capital Trends survey, found building "human advantage" is now as critical as managing technology — and specifically the ability to learn, adapt, and work effectively with other people in real time.
For multinational teams, trust is harder to build where there are language gaps, cultural differences, and time zone issues — and investing in real language skills (rather than relying on AI translation) is one of the best ways to rebuild that trust.
Communication and human skills are now the career differentiator. Companies working with international teams don’t expect technical staff to be perfect in English — but they do expect clear communication, professional writing, and the ability to manage difficult conversations. These skills directly affect how quickly you grow in your role.
*Think of one difficult work conversation you have had in the last month — a code review, a misunderstanding with a colleague, or a missed deadline. Write out how you handled it in English. Then find one phrase or word you could have used to communicate more clearly or professionally. Google it if you are unsure.
- English
Six tips for speaking English internationally, focused on using English as a shared international language.
Always check you’ve understood correctly, participate actively…that’s good communication.
Checking understanding isn’t only the responsibility of the listener. When you're talking, check people have understood you - What would you do in that situation? What do you think?
Simple, clear English is better than complex English in international teams. Think of your listeners and speak simply, slowly, and clearly. Avoid idioms, jargon and potentially unfamiliar abbreviations.
Repeat key points, paraphrase or rephrase them - this helps people remember. Use expressions 'that is…' and 'in other words…'.
Make sure your pronunciation is clear. Everyone has an accent, it’s YOU, but apart from that it doesn’t matter. What matters is that people understand you. Being understandable is called 'intelligibility'. Intelligibility – not accent – is the big one to work on.
- A bit of Fun
- What to Do
Before you commit AI-generated code — read it line by line and make sure you can explain each part in plain English. This one habit protects your professional reputation.
Record yourself for 60 seconds — use Claude or ChatGPT to generate a small code solution, then explain it out loud as if talking to your team lead. If you hesitate or get lost, do it again, that's your practice target this week.
Write 3 sentences about one AI tool you use — what it does, when you use it, and what you check before trusting its output. Say them out loud. Interviewers and managers are asking exactly this now.
Think of one difficult work conversation from last month — a code review, a missed deadline, a misunderstanding. Write how you handled it in English. Find one phrase you could have used more clearly. Google it, rephrase it. Do it again.
In your next message or meeting, cut the idioms and abbreviations — write or speak simply, slowly, and clearly. Your goal is not a perfect accent. Your goal is to be understood. That's called intelligibility, and it's the skill that opens doors in international teams.
- Quote of the week
“A little bit of IT English…EVERY WEEK!”
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