Problem Solving

3 posts

toss4 min readCurated summary

Tips for Growing the Skills to Solve Cross-Functional Technical Problems

As organizations grow, their hardest technical problems increasingly arise between teams rather than within them. These cross-functional, cross-domain problems cannot be solved through more meetings, status updates, or risk tracking alone; they require redefining the problem, structuring it, and creating an execution model that moves people to action. The post presents Toss’s Technical Program Manager (TPM) role as an example of this approach. ## Why Cross-Functional Problems Persist - Individual teams may perform well while the organization still fails to optimize as a whole. - Technical issues often span product, infrastructure, data, security, operations, strategy, and organizational design. - Common symptoms include: - Unclear ownership - Missing decision-makers - Conflicting priorities - Dependencies across multiple teams - Important “gray areas” with no formal owner - As organizations mature, these boundary problems become more common because team responsibilities become clearer while cross-team gaps remain. ## The Core Principle: Redefine the Problem - Cross-functional technical problems are not solved by increasing management activity. - More frequent meetings, status reports, risk registers, and stakeholder alignment may be useful but often address symptoms. - The real bottleneck may be: - An absent decision structure - Ambiguous ownership - Conflicting priorities - A system that does not connect individual team efforts - Effective problem-solving starts by identifying the underlying issue rather than merely describing delays or communication problems. ## Capabilities Required to Solve These Problems ### Reframing the Problem - Identify why schedules slip or decisions stall. - Determine which responsibilities or decisions are missing. - Find the structural conditions that repeatedly create the same gap. - Without accurate problem definition, organizations continue managing symptoms. ### Turning Ambiguity into Structure - Make decisions, options, responsibilities, dependencies, and sequencing explicit. - Break complex issues into manageable units. - Replace vague discussion with concrete decision points and ownership. ### Exercising Strategic Judgment - Distinguish temporary incidents from recurring structural problems. - Decide whether the issue can be solved within one team or requires broader intervention. - Assess whether immediate action is necessary. - Prioritize problems that improve the organization’s overall execution capability. ### Converting Plans into Execution - Identify who must act and which decisions must happen first. - Remove blockers and turn unclear discussions into explicit decisions. - Secure agreement on action plans and ensure those actions actually occur. - The goal is not merely to monitor execution, but to make execution possible. ### Influencing Without Formal Authority - Cross-functional work rarely succeeds through hierarchy alone. - TPMs need trust, sound judgment, and the ability to translate between teams with different goals and constraints. - Their influence should come from credibility and problem-solving results rather than title. ### Seeing People and Structure Together - Many technical problems are also caused by unclear roles, unsuitable team structures, or outdated operating mechanisms. - Effective intervention may require changing processes, redistributing responsibilities, or involving leadership—not just modifying technology. ## A Practical Starting Point for Less Autonomous Organizations - **Solve a small, concrete bottleneck first:** Demonstrate that involvement makes work clearer and faster. - **Add structure within existing coordination duties:** Use meetings and schedule management to expose decisions, dependencies, and blockers. - **Clarify ownership in a limited scope:** Define the real owner, decision rights, and completion criteria for a small initiative. - **Build evidence through successful cases:** Organizations often recognize new roles through demonstrated results rather than role descriptions. ## Important Cautions - Coordination remains valuable, but it should serve problem-solving rather than become the goal. - Lack of formal authority does not mean lack of influence; trust, structure, and results can be more powerful. - Introducing an idealized role too quickly may trigger resistance. It is better to make the approach work within the organization’s current environment and expand from proven examples. The central recommendation is to stop treating cross-functional technical problems as coordination exercises. First ask what the real bottleneck is, who is missing, and what execution structure would enable progress; then use that understanding to drive concrete organizational change.

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kakaoOriginal article

[AI_TOP_10 (opens in new tab)

The AI TOP 100 contest was designed to shift the focus from evaluating AI model performance to measuring human proficiency in solving real-world problems through AI collaboration. By prioritizing the "problem-solving process" over mere final output, the organizers sought to identify individuals who can define clear goals and navigate the technical limitations of current AI tools. The conclusion of this initiative suggests that true AI literacy is defined by the ability to maintain a "human-in-the-loop" workflow where human intuition guides AI execution and verification. ### Core Philosophy of Human-AI Collaboration * **Human-in-the-Loop:** The contest emphasizes a cycle of human analysis, AI problem-solving, and human verification. This ensures that the human remains the "pilot" who directs the AI engine and takes responsibility for the quality of the result. * **Strategic Intervention:** Participants were encouraged to provide AI with structural context it might struggle to perceive (like complex table relationships) and to perform data pre-processing to improve AI accuracy. * **Task Delegation:** For complex iterative tasks, such as generating images for a montage, solvers were expected to build automated pipelines using AI agents to handle repetitive feedback loops while focusing human effort on higher-level strategy. ### Designing Against "One-Shot" Solutions * **Low Barrier, High Ceiling:** Problems were designed to be intuitive enough for anyone to understand but complex enough to prevent "one-shot" solutions (the "click-and-solve" trap). * **Targeting Technical Weaknesses:** Organizers intentionally embedded technical hurdles that current LLMs struggle with, forcing participants to demonstrate how they bridge the gap between AI limitations and a correct answer. * **The Difficulty Ladder:** To account for varying domain expertise (e.g., OCR experience), problems utilized a multi-part structure. This included "Easy" starting questions to build momentum and "Medium" hint questions that guided participants toward solving the more difficult "Killer" components. ### The 4-Pattern Problem Framework * **P1 - Insight (Analysis & Definition):** Identifying meaningful opportunities or problems within complex, unstructured data. * **P2 - Action (Implementation & Automation):** Developing functional code or workflows to execute a defined solution. * **P3 - Persuasion (Strategy & Creativity):** Generating logical and creative content to communicate technical solutions to non-technical stakeholders. * **P4 - Decision (Optimization):** Making optimal choices and simulations to maximize goals under specific constraints. ### Quality Assurance and Score Calibration * **4-Stage Pipeline:** Problems moved from Ideation to Drafting (testing for one-shot immunity), then to Candidate (analyzing abuse vulnerabilities), and finally to a Final selection based on difficulty balance. * **Cross-Model Validation:** Internal and alpha testers solved problems using various models including Claude, GPT, and Gemini to ensure that no single tool could bypass the intended human-led process. * **Effort-Based Scoring:** Instead of uniform points, scores were calibrated based on the "effort cost" and human competency required to solve them. This resulted in varying total points per problem to better reflect the true difficulty of the task. In the era of rapidly evolving AI, the ability to "use" a tool is becoming less valuable than the ability to "collaborate" with it. This shift requires a move toward building automated pipelines and utilizing a "difficulty ladder" approach to tackle complex, multi-stage problems that AI cannot yet solve in a single iteration.

figma3 min readCurated summary

Meet the nonprofit training formerly incarcerated people for careers in UX design | Figma Blog

CROP Organization is a nonprofit helping formerly incarcerated people rebuild their lives through holistic reentry support and UX design training. Its year-long Ready 4 Life program combines personal development, career preparation, employment services, and stable housing, using Figma to open pathways into tech. The organization argues that access to education, tools, mentorship, and opportunity can reduce recidivism and challenge assumptions about what formerly incarcerated people can achieve. ## CROP’s Mission and Ready 4 Life Program - Creating Restorative Opportunities and Programs (CROP) was founded in Oakland by people with extensive personal experience of incarceration. - The organization addresses gaps in reentry services, where housing, employment, counseling, and other support are often fragmented. - Its Ready 4 Life program provides: - Personal development - Career training - Employment services - Stable housing - The first cohort included 12 fellows living in apartments on CROP’s West Oakland residential and training campus. - Fellows also receive personal coaches and a monthly stipend. ## Founding the Organization Through Lived Experience - CROP’s founders collectively spent roughly 100 years incarcerated. - After one founder’s release exposed serious weaknesses in existing reentry services, the group designed a more integrated, “wraparound” model. - Jason Bryant describes education as transformative; during 20 years in prison, he earned a bachelor’s degree, two master’s degrees, and certification as a drug and alcohol counselor. - CROP seeks to help participants build skills and stability rather than simply return them to disconnected support systems. ## UX Design as a Career Path - CROP trains fellows in UX design using Figma. - Instructor Alexis Bustos joined after five years as a UX designer because she wanted to make a direct contribution to diversity and inclusion in technology. - She sees fellows’ storytelling, problem-solving, and ability to connect with people as valuable strengths in UX. - According to Bustos, the fellows primarily need technical training and access to tools—not a fundamentally different set of abilities. ## Reducing Recidivism Through Opportunity - The program aims to help address California’s approximately 50% recidivism rate. - California spends about $106,000 per incarcerated person annually, while only 3.4% of that spending goes toward rehabilitation. - CROP says its program costs roughly half the per-person cost of incarceration. - In 2021, the organization secured a three-year, $28.5 million partnership with California after presenting its plans to Governor Gavin Newsom. ## Challenging Social Stigma - CROP’s broader mission is to demonstrate that formerly incarcerated people can transform their lives when given meaningful opportunities and resources. - The program combines practical support with professional development so fellows can envision futures beyond the limitations imposed by criminal records. - By preparing participants for careers in technology, CROP also seeks to create new entry points into an industry where formerly incarcerated people have historically had limited access. CROP’s approach suggests that successful reentry requires more than isolated services: stable housing, coaching, education, technical skills, and employment support must work together. Programs that pair this holistic foundation with real career opportunities can benefit both participants and the communities they return to.

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