Training & Development

One lesson that has served me well over the years: Don’t carry bad blood. In any long career you will be misunderstood. You’ll disagree. You’ll feel wronged. Sometimes even betrayed. Decisions won’t go your way. People will make calls you don’t agree with. The temptation is to keep score. But resentment is expensive. It clouds judgment. It narrows perspective. It limits future opportunity and is neither rational nor helpful. I’ve learned that making things right is both good relational practice and good business practice. Industries are small. Reputations compound. People you disagree with today may become partners tomorrow. And often, when you sit down and have the hard conversation, you discover the situation was more complex than you initially believed. There is real freedom in choosing not to carry offense. In picking up the phone and saying, “Let’s reset.” Over time, it builds a network of people who know you value relationships. Early in my career I valued results over relationships. Now, I know relationships trump results. Relationships are far more enduring and valuable than the near term results I used to favor. That’s good for business. More importantly, it’s good for the soul.

One skill separates great communicators from average ones: Perspective-taking. The ability to see things from someone else’s point of view. But most people do it wrong. Here’s how to do it right, especially when you’re leading or being led: When you’re the boss, persuading down: You’re trying to convince Maria on your team to do something different. She’s pushing back. Your instinct might be to assert your authority. But that’s a mistake. Here’s why… Research shows: The more powerful you feel, the worse your perspective-taking becomes. More power = less understanding. So if you want to persuade Maria, don’t lean into your title. Do the opposite: dial your power down, just briefly. Try this: Before the next conversation, remind yourself: Maria has power too. I need her buy-in. Maybe she sees something I don’t. Lower your feelings of power to raise your perspective. From that place, ask: → What does she see that I’m missing? → What might be in her way? → What’s a win-win outcome? That shift changes the entire dynamic. Instead of steamrolling, you’re collaborating. And that’s how you earn trust and results. Now flip it. You’re the employee persuading your boss. It’s a high-stakes moment. You’re nervous. So do you appeal to emotion? No. Drop the feelings. Focus on interests. Here’s the key question: “What’s in it for them?” Not how you feel. Not your big dream. → Will it save time? → Improve performance? → Help them hit their goals? Make it about their world, not yours. Why? Because every boss has a mental shortcut: → Does this employee make my life easier or harder? Be the person who brings clarity, ideas, and upside. Not complaints, drama, or friction. In summary: → Persuading down? Dial down your power to see clearer. → Persuading up? Focus on their interests, not your emotions. Perspective-taking is a superpower, if you learn how to use it. Now practice, practice, practice.

Disclosing the full list of datasets used to train IBM LLMs Granite 3.0. This is true transparency - no other LLM provider shares such detailed information about their training datasets. WEB Data - FineWeb: More than 15T tokens of cleaned and deduplicated English data from CommonCrawl. - Webhose: Unstructured web content in English converted into machine-readable data. - DCLM-Baseline: A 4T token / 3B document pretraining dataset that achieves strong performance on language model benchmarks. CODE - Code Pile: Sourced from publicly available datasets like GitHub Code Clean and StarCoderdata. - FineWeb-Code: Contains programming/coding-related documents filtered from the FineWeb dataset using annotation. - CodeContests: Competitive programming dataset with problems, test cases, and human solutions in multiple languages. DOMAIN - USPTO: Collection of US patents granted from 1975 to 2023. - Free Law: Public-domain legal opinions from US federal and state courts. - PubMed Central: Biomedical and life sciences papers. - EDGAR Filings: Annual reports from US publicly traded companies over 25 years. MULTILINGUAL - Multilingual Wikipedia: Data from 11 languages to support multilingual capabilities. - Multilingual Webhose: Multilingual web content converted into machine-readable data feeds. - MADLAD-12: Document-level multilingual dataset covering 12 languages. INSTRUCTIONS - Code Instructions Alpaca: Instruction-response pairs about code generation problems. - Glaive Function Calling: Dataset focused on function calling in real scenarios. ACADEMIC - peS2o: A collection of 40M open-access academic papers for pre-training. - arXiv: Scientific paper pre-prints posted to arXiv. Full author acknowledgement can be found here. - IEEE: Technical content from IEEE acquired by IBM. TECHNICAL - Wikipedia: Technical articles sourced from Wikipedia. - Library of Congress Public Domain Books: More than 140,000 public domain English books. - Directory of Open Access Books: Publicly available technical books from the Directory of Open Access Books. - Cosmopedia: Synthetic textbooks, blog posts, stories, and WikiHow articles. MATH - OpenWebMath: Mathematical text from the internet, filtered from 200B HTML files. - Algebraic-Stack: Mathematical code dataset including numerical computing and formal mathematics. - Stack Exchange: User-contributed content from the Stack Exchange network. - MetaMathQA: Dataset of rewritten mathematical questions. - StackMathQA: A curated collection of 2 million mathematical questions from Stack Exchange. - MathInstruct: Focused on chain-of-thought (CoT) and program-of-thought (PoT) rationales for mathematical reasoning. - TemplateGSM: Collection of over 7 million grade-school math problems with code and natural language solutions. BOOM!

Louder for the people at the back 🎤 Many organisations today seem to have shifted from being institutions that develop great talent to those that primarily seek ready-made talent. This trend overlooks the immense value of individuals who, despite lacking experience, possess a great attitude, commitment, and a team-oriented mindset. These qualities often outweigh the drawbacks of hiring experienced individuals with a fixed and toxic mindset. The best organisations attract talent with their best years ahead of them, focusing on potential rather than past achievements. Let’s be clear this is more about mindset and willingness to learn and unlearn as apposed to age. To realise the incredible potential return, organisations must commit to creating an environment where continuous development is possible. This requires a multi-faceted approach: 1. Robust Training Programmes: Employers should invest in comprehensive training programmes that equip employees with the necessary skills for their roles. This includes on-the-job training, mentorship programmes, online courses, and workshops. 2. Redefining Hiring Criteria: Organisations should revise their hiring criteria to focus more on candidates’ potential and willingness to learn rather than solely on prior experience or formal qualifications. Behavioural interviews, aptitude tests, and probationary periods can help assess a candidate's ability to learn and adapt. 3. Partnerships with Educational Institutions: Companies can collaborate with educational institutions to design curricula that align with industry needs. Apprenticeship programmes, internships, and cooperative education can bridge the gap between academic learning and practical job skills. 4. Lifelong Learning Culture: Encouraging a culture of lifelong learning within organisations is crucial. Employers should provide ongoing education opportunities and support for professional development. This includes continuous skills assessment and access to resources for upskilling and reskilling. 5. Inclusive Recruitment Practices: Employers should implement inclusive recruitment practices that remove biases and barriers. Blind recruitment, diversity quotas, and targeted outreach programmes can help ensure that diverse candidates are given a fair chance. By implementing these measures, organisations can develop a workforce that is adaptable, innovative, and resilient, ensuring sustainable success and growth.

“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build. Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention. The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention! Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on. The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience. When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful. [Truncated for length. Full text: https://lnkd.in/gKDQ6H9s]

Most L&D professionals learned the Kirkpatrick Model early on. Fewer have seen it applied beyond Level 1. Here's what each level can actually look like when you put it into practice, not just the textbook definition. ✨ Level 1: Reaction 🔹 Textbook version: Did learners find the training engaging and worth their time? ✅ In practice: Instead of "Did you enjoy this session?", ask "Was this relevant to the work you do?" and "Could you apply this right away?" ✅ Metric to track: Relevance and applicability ratings, not just satisfaction scores. ✨ Level 2: Learning 🔹 Textbook version: Did learners gain the intended knowledge or skills? ✅ In practice: Replace recall-based quizzes with scenario-based checks. Can the learner apply the concept to a situation they'd actually face? ✅ Metric to track: Pre/post assessment scores on scenario-based questions, not just "did you pass the quiz." ✨ Level 3: Behavior 🔹 Textbook version: Are learners applying what they learned on the job? ✅ In practice: 30/60/90-day check-ins, manager observations, or peer feedback on whether the new behavior is showing up in real work. ✅ Metric to track: % of participants demonstrating the target behavior, based on manager or peer input, not self-reported confidence. ✨ Level 4: Results 🔹 Textbook version: Did the training impact business outcomes? ✅ In practice: Pick one business metric the program was meant to influence, before you build it, not after, and track the change. ✅ Metric to track: Movement in that specific KPI (error rates, time-to-productivity, conversion rates, retention) compared to a baseline. Most programs are measured thoroughly at Level 1 and barely at all beyond it. But Levels 3 and 4 are where the "did this actually matter" conversation happens, and they are also where L&D earns a seat at the table. Which level does your organisation measure consistently, and which one do you wish you could measure better? #LearningAndDevelopment #LnD #KirkpatrickModel #TrainingEvaluation #InstructionalDesign #LearningMeasurement #TrainingAndDevelopment #LnDStrategy

I just watched a brilliant young mind quit after his first performance review.  The system didn't fail, it worked exactly as designed. And that's the problem. A close friend's son called me yesterday asking for advice. This kid has always been exceptional - top of his class, and one of the most hardworking young minds I know. He joined a company last year, excited to prove himself. His first performance review just happened. They put him on a PIP for "team collaboration issues." Here's what actually happened that past year: + On-time, flawless project delivery. + Zero complaints from stakeholders. + Often stayed late to get things right. But he wasn’t loud. He didn’t hang around in Slack threads and coffee chats or networked just for the sake of being visible. He focused on the work. And that somehow became a problem. When he called me, his voice was shaking. "I keep questioning myself. Maybe I really am terrible at my job." Just imagine an A-player, now doubting his entire future because our review systems punish introverts, misfit metrics, and non-traditional brilliance. I told him what I'm telling you: You're not the problem, kid. The system is. Four decades in this industry, and this still breaks my heart every time.  We're crushing exceptional talent with processes designed for a different era. We measure yesterday's activities instead of tomorrow's potential. The best leaders understand that real performance happens in real-time, not annual reviews. They coach continuously, celebrate wins immediately, and address challenges before they destroy confidence. ✅ Netflix eliminated performance reviews entirely.  ✅ Adobe replaced them with ongoing conversations.  ✅ Google shifted to quarterly goals with continuous feedback. These aren't experiments, they're competitive advantages. While traditional companies waste months on review documents nobody reads, smart organisations invest that time in actual development conversations that drive results. We need to replace annual reviews with monthly check-ins that matter. And most importantly, replace the assumption that people need to be "reviewed" like products with the understanding they need to be supported, challenged, and trusted to grow. That young man will find a company that values his work ethic over his small talk skills. His former employer will keep wondering why they can't retain talent while using the same broken processes. The difference will transform one organisation and devastate the other. So, stop managing performance like it's a quarterly report. Start enabling it like it's a human being's career and dreams. #performancereviews #thoughtleadership

In 2016, as I was just finding my footing as a #diversity, #equity, and #inclusion practitioner, I read an article titled, "Why Diversity Programs Fail" and learned that DEI work was not as straightforward as I had thought. From hundreds of interviews and 30 years of data from more than 800 US companies, sociologists Frank Dobbin and Alexandra Kalev found that the usual DEI interventions—mandatory diversity training, job tests, and grievance procedures—tended to REDUCE the representation of women, Black, Latine, and Asian managers. They detailed the unintended consequences of companies that deploy these intiatives: resentment and backlash, double standards, retaliation and more. There are many DEI initiatives and programs that work. Dobbin and Kalev found that programs that drive intergroup contact and draw on people's desire to look good meaningfully increase representation. Other research has found that standardizing hiring processes reduces hiring discrimination, developing competency criteria mitigates bias on promotions outcomes and feedback, designing the workday so people spend more time with others different from them lowers prejudice and increases belonging, and so on. But the $9.4-billion dollar DEI industry didn't get as big as it did by focusing on these evidence-based practices. Change management takes time, money, and coordination, and employers are often leery to take this approach unless forced. Instead, companies opt for one-off initiatives that they can breadcrumb their way toward the bare minimum—interventions that, unfortunately, tend to be either wholly ineffective or activate hostility and resentment that turn the clock back. As a solo practitioner in the late 2010s who was offering precisely the one-time trainings and workshops that were in-demand, this was all overwhelming to me. I knew that in my heart this work was important, but I also wanted my tactics to be effective. If I wasn't actually reducing discrimination, increasing retention, supporting thriving, then I wasn't doing my job properly. If the impact lay in adjacent work—coaching leaders to understand the role they had to play, working alongside HR teams to design standardized processes to mitigate bias, supporting employee resource groups to set better boundaries, designing impact measurement and infrastructure to support long-term behavior change, and so on—then I had to go in those directions. DEI is far from dead, but we can't pretend that the performative pre-backlash status quo was the best we had to offer. As I spoke to Vox in a recent article, this is our moment to double down on our impact, not just good intentions. We can't just aspire to design workplaces that are fair, accessible, inclusive, and representative for all; we have to actually demonstrate success, measurably and tangibly. Anti-DEI activists peddle fear. We beat them with hope—and the proof that we're building a better world for everyone than they could imagine.

I was embarrassed when we onboarded new hires. I don't have fancy collateral. No welcome videos. No searchable database. Just a bunch of Google Docs. (And a lot of my time) When I hired our first employee, I gave them these docs as part of their onboarding. I apologized that I didn’t have something fancier for them. Mentioned how we're a start-up with limited resources. But they told me they were amazed at the level of detail. And they wished they had something like that in their previous jobs. They came from a big company so my first thought was: "There's no way that's true." "They are probably used to more robust onboarding." But then our 2nd hire said the same thing. Then the 3rd. And so on. Even people outside my company applauded our process. My key takeaways: ➟ Many companies don't prioritize onboarding properly. ➟ You don't need flashy tools to set up new hires for success. Just provide the right information in a clear, organized way. Important elements of good onboarding: • Clear documentation covering roles, expectations, processes • A structured timeline for taking in information • Assigning a mentor to provide guidance • Scheduled check-ins to address questions It’s easy to assume more complexity means better onboarding. But from my experience, the basics done right go a long way. What do you think makes for an effective onboarding experience? Share below ⬇ ---- P.S. If this resonated with you, ♻ reshare to your network

For my first 16 years in tech sales, I averaged 240K/year. In my last 4 years, I averaged 720K/year. I did this by using an approach I call Yo-yo selling: 🪀 It’s how you win large, complex enterprise deals by building credibility with senior executives at the beginning of a sales cycle. This will save you months of spending time with mid or lower level Directors on a deal cycle, only to have your deal stall because it's not a priority for Executives. Here’s the concept: You start at the top, get senior level sponsorship for a deep discovery, drop down into the business, then bounce back up with a report of findings. This is the process I've used for nearly every 7-figure deal I've ever closed. Step 0: Research before outreach Before asking for time, I do deep strategic research. Earnings calls. Investor decks. Press releases. Executive interviews. I also spend time talking to their team to see if the problem that I solve exists in their company. Using that research, I build a Point of View that connects their top business goals to real execution gaps. This earns executive time. Today, AI tools like ChatGPT make this easier than ever. What used to take hours now takes minutes. If you skip this step, you lose your edge. Step 1: Prospect to the top and gain their sponsorship to engage Lead with your POV. The key is to teach them something new about their business which they aren't already aware of, and show them how it's putting their highest level goals at risk. If they lean in, offer up a deep discovery with your team and their team. Lock in a date to come back for a readout. Have them assign a project manager to help you coordinate Step 2: Drop down Once you have executive sponsorship, meet with their team. The key is to have the Exec sponsor send out a note to their team explaining what it's for. This will keep the assessment moving forward. Study workflows. Capture friction. Collect quotes. Do not pitch. Just listen. Step 3: Bounce back up Bring it all together in an executive summary. Show how their vision connects directly to what’s broken below. Present a focused business case. Build a custom demo. Create a roadmap and implementation plan. That’s where deals close. Real example from my career At Berkshire Hathaway HomeServices, we were told “no” on a point solution. Instead of walking away, I stepped back and asked what the company really needed. After deep research, I re-engaged the COO with a transformation POV centered on the experience of 50,000+ agents. The result was one of the largest new logo deals in Salesforce history. But Yo-yo selling alone isn’t enough. Because it's hard to execute and takes patience. Top performers also master their mindset, habits, and discipline. That’s why I put together a free masterclass for sellers who want to break into the top 1 percent. 👉 Watch the free training here: https://lnkd.in/eWD8mTqH If you’re serious about enterprise sales, this will change how you sell.