Agentic retrieval is considered reliable only when users can verify not just the final answer but the entire chain of decisions that produced it. The most mature systems treat verification as an integral part of the workflow, giving users visibility into what the agent saw, how it interpreted that information, which tools it invoked, and why it converged on a particular conclusion. When these mechanisms work together, they transform a stochastic, improvisational agent into something that behaves more like an auditable, instrumented pipeline.

The first layer of verification comes from detailed traces of the agent’s reasoning steps. These traces reveal the sequence of tool calls, the inputs and outputs of each step, and the logic that guided the agent’s choices. Even though the internal chain of thought remains abstracted, the user still sees a faithful record of the agent’s actions: how it decomposed the query, which retrieval strategies it attempted, and where it may have misinterpreted evidence. In a drone analytics context, this might show the exact detector invoked, the confidence thresholds applied, and the SQL filters used to isolate a particular geospatial slice. This level of transparency allows users to diagnose inconsistencies and understand why the agent behaved differently across runs.

A second layer comes from grounding and citation tools that force the agent to tie its conclusions to specific pieces of retrieved evidence. Instead of producing free-floating assertions, the agent must show which documents, image regions, database rows, or vector-search neighbors support its answer. This grounding is especially important in multimodal settings, where a single misinterpreted bounding box or misaligned embedding can change the meaning of an entire mission. By exposing the provenance of each claim, the system ensures that users can trace the answer back to its source and evaluate whether the evidence truly supports the conclusion.

Deterministic tool wrappers add another stabilizing force. Even if the model’s reasoning is probabilistic, the tools it calls—detectors, SQL templates, vector-search functions—behave deterministically. Fixed seeds, fixed thresholds, and fixed schemas ensure that once the agent decides to call a tool, the tool’s behavior is predictable and reproducible. This separation between stochastic planning and deterministic execution is what allows agentic retrieval to feel stable even when the underlying model is not.

Schema and contract validators reinforce this stability by ensuring that every tool call conforms to expected formats. They reject malformed SQL, incorrect parameter types, invalid geospatial bounds, or unsafe API calls. When a validator blocks a step, the agent must correct its plan and try again, preventing silent failures and reducing the variability that comes from poorly structured queries. These validators act as guardrails that keep the agent’s behavior within predictable bounds.

Some systems go further by introducing counterfactual evaluators that explore alternative retrieval paths. These evaluators run parallel or fallback queries—different detectors, different chunking strategies, different retrieval prompts—and compare the results. If the agent’s initial path diverges too far from these alternatives, it can revise its reasoning or adjust its confidence. This reduces sensitivity to small prompt variations and helps the agent converge on answers that are robust across multiple retrieval strategies.

Self-critique layers add yet another dimension. These evaluators score the agent’s output using task-specific rubrics, consistency checks, cross-model agreement, or domain constraints. In aerial imagery, for example, a rubric might flag an object that is physically impossible given the frame’s scale or context. By forcing the agent to evaluate its own output before presenting it to the user, the system catches errors that would otherwise appear as unpredictable behavior.

All of these mechanisms culminate in human-readable execution summaries that distill the entire process into a coherent narrative. These summaries explain which tools were used, what evidence was retrieved, how the agent reasoned through the problem, and where uncertainty remains. They give users a clear sense of the workflow without overwhelming them with raw traces, and they reinforce the perception that the system behaves consistently even when the underlying model is improvisational.

Together, these verification tools form a feedback loop in which the agent proposes a plan, validators check it, deterministic tools execute it, grounding ties it to evidence, counterfactuals test its robustness, evaluators critique it, and summaries explain it. This loop transforms agentic retrieval from a black-box improvisation into a transparent, auditable process. The deeper shift is that users stop relying on the agent’s answers alone and begin trusting the process that produced them. In operational domains like drone analytics, that shift is what makes agentic retrieval predictable enough to use with confidence.

Alternate source of truth and observability pipelines are often ignored from verification mechanisms but they are powerful reinforcers. Traditional mechanisms relying on structured and non-structured data direct queries can at least provide a grounding basis as much as it was possible to use online literature via a grounding api call. Custom metrics and observability pipelines also provide a way to measure drifts when none is anticipated. Lastly, error corrections and their root causes help to understand the underlying errors that can help to keep a system verified and operating successfully.

 TorchLean from Caltech is an attempt to close a long‑standing gap between how neural networks are built and how they are formally reasoned about. Instead of treating models as opaque numerical engines, it treats them as mathematical objects with precise, inspectable semantics. The work begins from a simple but powerful observation: most verification pipelines analyze a network outside the environment in which it runs, which means that subtle differences in operator definitions, tensor layouts, or floating‑point behavior can undermine the guarantees we think we have. TorchLean eliminates that gap by embedding a PyTorch‑style modeling API directly inside the Lean theorem prover and giving both execution and verification a single shared intermediate representation. This ensures that the network we verify is exactly the network we run. arXiv.org

The framework builds its foundation on a fully executable IEEE‑754 Float32 semantics, making every rounding behavior explicit and proof‑relevant. On top of this, it layers a tensor system with precise shape and indexing rules, a computation‑graph IR, and a dual execution model that supports both eager evaluation and compiled lowering. Verification is not an afterthought but a first‑class capability: TorchLean integrates interval bound propagation, CROWN/LiRPA linear relaxations, and α, β‑CROWN branch‑and‑bound, all with certificate generation and checking. These tools allow one to derive certified robustness bounds, stability guarantees for neural controllers, and derivative bounds for physics‑informed neural networks. The project’s authors demonstrate these capabilities through case studies ranging from classifier robustness to Lyapunov‑style safety verification and even a mechanized proof of the universal approximation theorem. Github

What makes TorchLean particularly striking is its ambition to unify the entire lifecycle of a neural network—definition, training, execution, and verification—under a single semantic‐first umbrella. Instead of relying on empirical testing or post‑hoc analysis, the framework encourages a world where neural networks can be reasoned with the same rigor as classical algorithms. The Caltech team emphasizes that this is a step toward a fully verified machine‑learning stack, where floating‑point behavior, tensor transformations, and verification algorithms all live within the same formal universe. LinkedIn

For our drone video sensing analytics framework, TorchLean offers a kind of structural clarity that aligns naturally with the way we already think about operational intelligence. Our system treats drone video as a continuous spatio‑temporal signal, fusing geolocation, transformer‑based detection, and multimodal vector search. TorchLean gives us a way to formalize the neural components of that pipeline so that robustness, stability, and safety guarantees are not just empirical observations but mathematically certified properties. For example, we could use its bound‑propagation tools to certify that our object‑detection backbone remains stable under small perturbations in lighting, altitude, or camera jitter—conditions that are unavoidable in aerial operations. Its explicit floating‑point semantics could help us reason for numerical drift in long‑duration flights or edge‑device inference. And its Lyapunov‑style verification tools could extend naturally to flight‑path prediction, collision‑avoidance modules, or any learned controller we integrate into our analytics stack.

More broadly, TorchLean’s semantics‑first approach complements our emphasis on reproducibility, benchmarking, and operational rigor. It gives us a way to turn parts of our pipeline into formally verified components, which strengthens our publication‑grade narratives and positions our framework as not just high‑performance but certifiably reliable. It also opens the door to hybrid workflows where our agentic retrieval and vision‑LLM layers can be paired with verified perception modules, creating a pipeline that is both intelligent and provably safe.

 This is a continuation of the previous article1.

Supporting code snippets:

1. Fetch_issues.py:

import requests, os, json

from datetime import datetime, timedelta

repo = os.environ["GITHUB_REPOSITORY"]

token = os.environ["GH_TOKEN"]

since = (datetime.utcnow() - timedelta(days=30)).isoformat()

url = f"https://api.github.com/repos/{repo}/issues?state=all&since={since}"

headers = {"Authorization": f"token {token}"}

issues = requests.get(url, headers=headers).json()

print(json.dumps(issues, indent=2))

2. embed_and_cluster.py:

import json, os

import numpy as np

from sklearn.cluster import KMeans

from openai import AzureOpenAI

client = AzureOpenAI(

    api_key=os.environ["AZURE_OPENAI_API_KEY"],

    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],

    api_version="2024-02-15-preview"

)

issues = json.load(open("issues.json"))

texts = [i["title"] + "\n" + i.get("body", "") for i in issues]

embeddings = []

for t in texts:

    e = client.embeddings.create(

        model="text-embedding-3-large",

        input=t

    ).data[0].embedding

    embeddings.append(e)

X = np.array(embeddings)

kmeans = KMeans(n_clusters=5, random_state=42).fit(X)

labels = kmeans.labels_

clusters = {}

for label, issue in zip(labels, issues):

    clusters.setdefault(int(label), []).append(issue)

print(json.dumps(clusters, indent=2))

3. generate_report.py:

import json, os

from openai import AzureOpenAI

client = AzureOpenAI(

    api_key=os.environ["AZURE_OPENAI_API_KEY"],

    azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],

    api_version="2024-02-15-preview"

)

clusters = json.load(open("clusters.json"))

prompt = f"""

You are an expert Terraform and Databricks architect.

Generate a monthly insights report with:

- Executive summary

- Top recurring problems

- Modules with the most issues

- Common root causes

- Suggested improvements to Terraform modules

- Hotspots in Databricks workspace deployments

- Action plan for next month

Data:

{json.dumps(clusters)}

"""

resp = client.chat.completions.create(

    model="gpt-4o-mini",

    messages=[{"role": "user", "content": prompt}],

    max_tokens=2000,

    temperature=0.2

)

print(resp.choices[0].message.content)

 An AI-generated monthly insights reports for Terraform GitHub repository can be realized by building a small automated pipeline, that puts all GitHub issues for the past month, embeds them into vectors, clusters and analyzes them, feeds the structured data into an LLM, produces a leadership friendly markdown report, and publishes it automatically via a Teams message. These are explained in detail below:

1. Data ingestion: A scheduled github action that runs monthly and fetches all issues created or updated in the last 30 days, their comments, labels, module references, and severity or impact indicators. This produces a json dataset like:

[

  {

    "id": 1234,

    "title": "Databricks workspace recreation on VNet change",

    "body": "Changing the VNet CIDR causes full workspace recreation...",

    "labels": ["bug", "module/databricks-workspace"],

    "comments": ["We hit this again last week..."],

    "created_at": "2026-02-01",

    "updated_at": "2026-02-05"

  }

]

2. And use Azure OpenAI embeddings (text-embedding-3-large) to convert each issue into a vector. The store has issue_id, embedding, module (parsed from labels or text), text (title + body + comments) and these can be stored in Pincone or a dedicated Azure AI Search (vector index)

For a simple implementation, pgvector is enough.

3. We can use unsupervised clustering to detect running themes: K-means, HDBScan, and agglomerative clustering. This lets you identify recurring problems, common root causes, hotspots in databricks deployments, and modules with repeated issues.

Sample output:

Cluster 0: Databricks workspace recreation issues (7 issues)

Cluster 1: Private endpoint misconfiguration (4 issues)

Cluster 2: Missing tags / policy violations (5 issues)

Cluster 3: Module version drift (3 issues)

4. This structured data is then fed into an LLM with a prompt like:

You are an expert Terraform and Azure Databricks architect.

Summarize the following issue clusters into a leadership-friendly monthly report.

Include:

- Top recurring problems

- Modules with the most issues

- Common root causes

- Suggested improvements to Terraform modules

- Hotspots in Databricks workspace deployments

- A short executive summary

- A recommended action plan for the next month

Data:

<insert JSON clusters + issue summaries>

And the LLM produced a polished Markdown report.

5. Sample output: for what’s presented to the leadership:

# Monthly Terraform Insights Report — February 2026

## Executive Summary

This month saw 19 issues across 7 Terraform modules. The majority were related to Databricks workspace networking, private endpoints, and tag compliance. Workspace recreation remains the most disruptive pattern.

## Top Recurring Problems

- Databricks workspace recreation due to VNet CIDR changes (7 issues)

- Private endpoint misconfiguration (4 issues)

- Missing required tags (5 issues)

- Module version drift (3 issues)

## Modules with the Most Issues

- module/databricks-workspace (9 issues)

- module/private-endpoints (4 issues)

- module/networking (3 issues)

## Common Root Causes

- Inconsistent module usage patterns

- Lack of lifecycle rules preventing accidental recreation

- Missing validation rules in modules

- Insufficient documentation around networking constraints

## Suggested Improvements

- Add `prevent_destroy` lifecycle blocks to workspace modules

- Introduce schema validation for required tags

- Add automated tests for private endpoint creation

- Publish module usage examples for networking patterns

## Hotspots in Databricks Deployments

- Workspace recreation triggered by minor networking changes

- Cluster policy misalignment with workspace settings

- Missing Unity Catalog configuration in new workspaces

## Action Plan for Next Month

- Refactor workspace module to isolate networking dependencies

- Add tag validation to all modules

- Create a “safe update” guide for Databricks workspaces

- Introduce CI checks for module version drift

6. That’s all!

 Drones operate with modular autonomy stacks: perception, localization, prediction, planning, and control. These modules rely heavily on real-time sensor input and preloaded maps, which can falter in dynamic or degraded conditions—poor visibility, occlusions, or unexpected traffic behavior. Our system introduces a complementary layer: a selective sampling engine that curates high-value video frames from vehicle-mounted or aerial cameras, forming a spatiotemporal catalog of environmental states and trajectory outcomes. This catalog becomes a living memory of the tour, encoding not just what was seen, but how the drone responded and what alternatives existed.  

By applying importance sampling, our copilot prioritizes frames with semantic richness—intersections, merges, pedestrian zones, or adverse weather—creating a dense vector space of contextually significant moments. These vectors are indexed by time, location, and scenario type, enabling retrospective analysis and predictive planning. For example, if a drone needs to calculate distance to a detour waypoint, this could help with similar geometry, overlay ground data, and suggest trajectory adjustments based on historical success rates.  

This retrieval is powered by agentic query framing, where the copilot interprets system or user intent—“What’s the safest merge strategy here?” or “How did similar vehicles handle this turn during rain?”—and matches it against cataloged vectors and online traffic feeds. The result is a semantic response, not just a path: a recommendation grounded in prior information, enriched by real-time data, and tailored to current conditions.  

Our analytics framework respects both autonomous and non-autonomous drone or swarm architectures, acting as a non-invasive overlay that feeds contextual insights into the planning module. It does not replace the planner—it informs it, offering scores, grounded preferences, and fallback strategies when primary sensors degrade.  

Moreover, our system’s customizability with online maps and traffic information integration allows for enriched drone video sensing applications. By leveraging standard 100m high point of reference for aerial images adjusted from online satellite maps of urban scenes, we detect objects that help beyond what custom models are trained for. In addition, the use of catalogued objects, grounded truth, and commodity models for analysis, we make this cost-effective. This help drones to evolve from perceive and plan to remember, compare and adapt which is aligned with the future of agentic mobility.  

 This is a summary of a book titled “Multi-Agent Reinforcement Learning: Foundations and Modern Approaches” written by Lukas Schäfer, Filippos Christianos and Stefano Albrecht and published by MIT Press in 2024. This book presents a systematic treatment of multi-agent reinforcement learning (MARL) by placing it at the intersection of reinforcement learning, game theory, and modern machine learning. It focuses on how multiple autonomous agents can learn, adapt, and coordinate in shared and potentially non-stationary environments.

A multi-agent system consists of several agents interacting with a common environment while pursuing individual or collective objectives. Each agent is capable of observing its surroundings, selecting actions according to a policy, and updating that policy based on feedback from the environment and the behavior of other agents. Unlike single-agent reinforcement learning, where the environment is typically assumed to be stationary, MARL settings are inherently dynamic: the environment evolves not only due to external factors but also as a direct consequence of other agents learning and changing their policies concurrently.

MARL extends reinforcement learning by replacing individual actions with joint actions and individual rewards with reward structures that depend on the combined behavior of multiple agents. Agents learn through repeated interaction over episodes, collecting experience about state transitions, rewards, and the strategies of others. Coordination is a central challenge, particularly in settings where agents have partial observability, conflicting goals, or limited communication. In some cases, agents must learn explicit or implicit communication protocols to align their behavior.

The theoretical foundations of MARL are closely tied to game theory. Multi-agent environments are commonly modeled as games, ranging from fully observable, deterministic settings to stochastic and partially observable games. In these models, agents assign probabilities to actions, and joint actions induce state transitions and rewards. Depending on the assumptions about observability, dynamics, and information availability, different classes of games—such as stochastic games or partially observable stochastic games—are used to formalize agent interaction.

Within these frameworks, multiple solution concepts may apply. The book discusses equilibrium notions such as minimax equilibrium in zero-sum games, Nash equilibrium in general-sum games, and correlated equilibrium, along with refinements including Pareto optimality, social welfare, fairness, and no-regret criteria. A key distinction from single-agent learning is that multi-agent systems may admit multiple optimal or stable policies, and convergence is often defined in terms of equilibrium behavior rather than a single optimal policy.

Training instability is a defining difficulty in MARL. Because agents learn simultaneously, the learning problem faced by any one agent changes as others update their policies, violating the stationarity assumptions underlying many reinforcement learning algorithms. Credit assignment further complicates learning, as rewards must be attributed appropriately across agents whose actions jointly influence outcomes. Performance is often evaluated by whether agents converge to a stable joint policy or to stable distributions over policies.

The book surveys a range of algorithmic approaches developed to address these challenges. Joint action learning explicitly models the value of joint actions, while agent modeling techniques attempt to predict the behavior of other agents based on observed histories. Policy-based methods optimize parameterized policies directly, and no-regret learning algorithms, such as regret matching, aim to eliminate systematically poor decisions over time. For specific classes of problems, such as zero-sum stochastic games, value iteration methods can be used to compute optimal state values with respect to joint actions.

Scalability and partial observability motivate the use of function approximation. Deep learning plays a central role in modern MARL by enabling agents to approximate value functions, policies, and belief states in high-dimensional and continuous environments. Neural network architectures such as multilayer perceptrons, convolutional neural networks, and recurrent neural networks are employed depending on whether the inputs are structured, visual, or sequential. These models are trained via gradient-based optimization to generalize beyond the limited set of states encountered during interaction.

The book distinguishes between different training and execution paradigms. Centralized training and execution assumes shared observations and policies but scales poorly and obscures individual responsibility for outcomes. Decentralized training and execution allows agents to learn independently but suffers from non-stationarity and limited coordination. A hybrid approach—centralized training with decentralized execution—seeks to combine the advantages of both by learning joint representations during training while allowing agents to act independently at deployment.

Overall, the book provides a detailed and technically grounded account of MARL, covering its theoretical foundations, algorithmic methods, and practical challenges, with an emphasis on learning and coordination in complex multi-agent environments.

Equations for cost analysis

 

 This is a summary of a book: “The DOSE Effect: Optimize Your Brain and Body by Boosting Your Dopamine, Oxytocin, Serotonin, and Endorphins” written by Tj Power, a neuroscientist and founder of DOSE Lab and published by Dey Street in 2025. This book examines how modern lifestyles disrupt the neurochemical systems that regulate motivation, mood, social connection, and stress resilience. Drawing on neuroscience and behavioral research, Power focuses on four key neurotransmitters—dopamine, oxytocin, serotonin, and endorphins—and explains how everyday habits influence their balance. He argues that chronic stress, insufficient sleep, poor diet, and constant digital stimulation interfere with these systems, leading to reduced motivation, emotional instability, and diminished well-being and proposes a healthier behavior and environment can allow the stimuli and responder to co-exist better.

Dopamine is presented as the primary driver of motivation and goal-directed behavior. It operates through a pleasure–pain mechanism in which effortful or uncomfortable actions initially produce strain but are followed by a sense of reward upon completion. This system evolved to reinforce survival-related behaviors, but in contemporary environments it is frequently overstimulated by effortless rewards such as highly processed food, alcohol, online shopping, and social media. These activities produce rapid dopamine spikes without corresponding effort, often followed by declines in mood and motivation. Repeated exposure to such stimuli narrows the range of activities that feel rewarding, contributing to compulsive behavior and reduced drive. In contrast, dopamine regulation is strengthened through sustained effort, structured routines, and engagement in meaningful pursuits. Consistently completing demanding tasks, maintaining order in one’s environment, and working toward long-term goals reinforces the association between effort and reward, gradually restoring motivation and psychological resilience.

He emphasizes that discipline is central to maintaining a stable dopamine system. Small, repeatable actions—such as maintaining personal routines or completing routine responsibilities—condition the brain to tolerate effort and delay gratification. Over time, this process supports a broader capacity for sustained focus and perseverance. Equally important is the presence of a clearly defined pursuit that provides direction and anticipation. Without an ongoing sense of purpose, achievements alone may fail to produce lasting satisfaction, whereas engagement in the pursuit itself supports motivation and emotional stability.

Oxytocin is described as the neurochemical foundation of social bonding, trust, and self-confidence. It is released during moments of affection, cooperation, and emotional connection, and it plays a critical role in forming and maintaining relationships. Low oxytocin levels are associated with loneliness, self-doubt, and social withdrawal, conditions that are exacerbated by habits such as excessive phone use, superficial online comparison, and reduced face-to-face interaction. Chronic deficits in social connection are portrayed as having significant psychological and physiological consequences. Conversely, oxytocin levels increase through acts of service, supportive relationships, and physical touch, all of which promote feelings of safety, belonging, and emotional stability. Regular interpersonal engagement and contribution to others’ well-being are presented as essential components of long-term mental health.

Serotonin is examined primarily through its connection to physical health and nutrition. Unlike other neurotransmitters, the majority of serotonin is produced in the gut, making dietary patterns and digestion central to emotional regulation. Diets high in ultra-processed foods and refined sugars are associated with fluctuations in mood, energy, and anxiety, while consistent, nutrient-dense eating supports more stable serotonin production. Sleep and exposure to natural light further influence serotonin levels, reinforcing circadian rhythms that promote calmness and sustained energy. Time spent outdoors, particularly in low-stimulation environments, is identified as a reliable way to improve mood, focus, and overall physiological balance.

Endorphins are characterized as the body’s primary mechanism for managing stress and physical discomfort. They evolved to mitigate pain and regulate emotional responses during periods of intense physical demand. In modern contexts, insufficient physical activity and prolonged sedentary behavior reduce endorphin release, leaving individuals more vulnerable to chronic stress and tension. Regular movement, particularly activities that combine strength, endurance, and short periods of high exertion, stimulates endorphin production and improves stress tolerance. Stretching and mobility practices further support this system by reducing physical tension and promoting relaxation.

Overall, he presents mental and emotional well-being as the outcome of interacting biological systems that are shaped by daily behavior. Rather than emphasizing short-term interventions or external solutions, it argues for sustained, effort-based habits that align with the brain’s underlying neurochemistry. By prioritizing purposeful work, meaningful relationships, nutritious food, regular movement, adequate sleep, and time in natural environments, individuals can create conditions that support more stable motivation, emotional regulation, and long-term psychological health.

 This is a summary of a book: “The DOSE Effect: Optimize Your Brain and Body by Boosting Your Dopamine, Oxytocin, Serotonin, and Endorphins” written by Tj Power, a neuroscientist and founder of DOSE Lab and published by Dey Street in 2025. This book examines how modern lifestyles disrupt the neurochemical systems that regulate motivation, mood, social connection, and stress resilience. Drawing on neuroscience and behavioral research, Power focuses on four key neurotransmitters—dopamine, oxytocin, serotonin, and endorphins—and explains how everyday habits influence their balance. He argues that chronic stress, insufficient sleep, poor diet, and constant digital stimulation interfere with these systems, leading to reduced motivation, emotional instability, and diminished well-being and proposes a healthier behavior and environment can allow the stimuli and responder to co-exist better.

Dopamine is presented as the primary driver of motivation and goal-directed behavior. It operates through a pleasure–pain mechanism in which effortful or uncomfortable actions initially produce strain but are followed by a sense of reward upon completion. This system evolved to reinforce survival-related behaviors, but in contemporary environments it is frequently overstimulated by effortless rewards such as highly processed food, alcohol, online shopping, and social media. These activities produce rapid dopamine spikes without corresponding effort, often followed by declines in mood and motivation. Repeated exposure to such stimuli narrows the range of activities that feel rewarding, contributing to compulsive behavior and reduced drive. In contrast, dopamine regulation is strengthened through sustained effort, structured routines, and engagement in meaningful pursuits. Consistently completing demanding tasks, maintaining order in one’s environment, and working toward long-term goals reinforces the association between effort and reward, gradually restoring motivation and psychological resilience.

He emphasizes that discipline is central to maintaining a stable dopamine system. Small, repeatable actions—such as maintaining personal routines or completing routine responsibilities—condition the brain to tolerate effort and delay gratification. Over time, this process supports a broader capacity for sustained focus and perseverance. Equally important is the presence of a clearly defined pursuit that provides direction and anticipation. Without an ongoing sense of purpose, achievements alone may fail to produce lasting satisfaction, whereas engagement in the pursuit itself supports motivation and emotional stability.

Oxytocin is described as the neurochemical foundation of social bonding, trust, and self-confidence. It is released during moments of affection, cooperation, and emotional connection, and it plays a critical role in forming and maintaining relationships. Low oxytocin levels are associated with loneliness, self-doubt, and social withdrawal, conditions that are exacerbated by habits such as excessive phone use, superficial online comparison, and reduced face-to-face interaction. Chronic deficits in social connection are portrayed as having significant psychological and physiological consequences. Conversely, oxytocin levels increase through acts of service, supportive relationships, and physical touch, all of which promote feelings of safety, belonging, and emotional stability. Regular interpersonal engagement and contribution to others’ well-being are presented as essential components of long-term mental health.

Serotonin is examined primarily through its connection to physical health and nutrition. Unlike other neurotransmitters, the majority of serotonin is produced in the gut, making dietary patterns and digestion central to emotional regulation. Diets high in ultra-processed foods and refined sugars are associated with fluctuations in mood, energy, and anxiety, while consistent, nutrient-dense eating supports more stable serotonin production. Sleep and exposure to natural light further influence serotonin levels, reinforcing circadian rhythms that promote calmness and sustained energy. Time spent outdoors, particularly in low-stimulation environments, is identified as a reliable way to improve mood, focus, and overall physiological balance.

Endorphins are characterized as the body’s primary mechanism for managing stress and physical discomfort. They evolved to mitigate pain and regulate emotional responses during periods of intense physical demand. In modern contexts, insufficient physical activity and prolonged sedentary behavior reduce endorphin release, leaving individuals more vulnerable to chronic stress and tension. Regular movement, particularly activities that combine strength, endurance, and short periods of high exertion, stimulates endorphin production and improves stress tolerance. Stretching and mobility practices further support this system by reducing physical tension and promoting relaxation.

Overall, he presents mental and emotional well-being as the outcome of interacting biological systems that are shaped by daily behavior. Rather than emphasizing short-term interventions or external solutions, it argues for sustained, effort-based habits that align with the brain’s underlying neurochemistry. By prioritizing purposeful work, meaningful relationships, nutritious food, regular movement, adequate sleep, and time in natural environments, individuals can create conditions that support more stable motivation, emotional regulation, and long-term psychological health.

 This is a summary of a book titled “Creativity in the Age of AI: Toolkits for the Modern Mind” written by Jerry (Yoram) Wind, Mukul Pandya and Deborah Yao and published by De Gruyter in 2025. Recommendation. This book examines creativity as a disciplined capability rather than a sporadic or innate talent, situating it within the contemporary context of artificial intelligence. The authors contend that creativity has become a central requirement for organizational effectiveness and long‑term competitiveness, particularly as AI technologies alter how problems are framed, explored, and solved. Their central is not that AI supplants human creativity, but th claim at it can extend and reinforce it when integrated into established cognitive, organizational, and analytical frameworks.

The book begins by establishing creativity as an essential component of business performance. Empirical research demonstrates a strong relationship between creative capability and outcomes such as revenue growth and market share, yet many organizations struggle to translate creativity into systematic practice. This gap, the authors argue, stems from persistent misconceptions: creativity is often treated as an unpredictable spark rather than as a process that can be deliberately cultivated. Drawing on longstanding research, the authors emphasize that creativity requires both novelty and usefulness, and that ideas only become creative when they are developed into practical and effective solutions.

To clarify how creativity functions, the authors revisit foundational models that remain relevant in the age of AI. Graham Wallas’s four‑stage framework—preparation, incubation, illumination, and verification—illustrates creativity as a progression from problem definition to refinement and implementation. Teresa Amabile’s componential theory further expands this view by identifying the interacting elements that support creativity: domain‑specific expertise, cognitive processes that enable creative thinking, intrinsic motivation, and an environment that encourages exploration and risk‑taking. Together, these models reinforce the authors’ view that creativity is the result of sustained effort shaped by both individual and contextual factors.

Advances in neuroscience provide additional support for this perspective. Research shows that creativity is supported by the interaction of three neural networks: the default mode network, which generates ideas; the executive control network, which evaluates and refines them; and the salience network, which mediates between exploration and judgment. Creativity depends on maintaining balance among these systems, a balance influenced by factors such as cognitive flexibility, intrinsic motivation, and psychological safety. Environmental conditions also matter. Spaces characterized by coherence, fascination, or comfort can support different phases of creative work, suggesting that creativity is shaped not only by mental processes but also by physical and social contexts.

Within this human-centered framework, AI is introduced as a complementary resource rather than a disruptive replacement. The authors position AI as an assistant and collaborator that can support creativity across execution, idea generation, and evaluation. By handling routine tasks, generating unconventional combinations, and providing analytical feedback, AI can expand the range of possibilities considered while allowing humans to focus on judgment and meaning. The example of Airbus’s use of generative design illustrates this dynamic: AI explored vast design spaces beyond human capacity, while engineers defined constraints and evaluated outcomes. The result was a solution that combined biological inspiration with engineering requirements, demonstrating how AI can augment, rather than diminish, human creative agency.

The authors also address the organizational challenges associated with adopting AI for creative work. Resistance to change, fear of failure, and limited resources can all impede progress. Rather than dismissing these concerns, the authors recommend examining their underlying causes and addressing them explicitly. Techniques such as pre‑mortem analysis can reduce uncertainty, while reframing obstacles as opportunities for reconsideration can help organizations move beyond entrenched habits. Creativity, in this view, requires not only tools but also cultural conditions that tolerate experimentation and learning.

A significant portion of the book is devoted to the role of mental models in shaping creative outcomes. Unexamined assumptions can constrain perception and limit the range of solutions considered. The authors argue that creativity depends on the continual reassessment of these models through techniques such as assumption reversal, analogical reasoning, and exposure to diverse perspectives. General‑purpose AI tools can assist by making implicit assumptions visible and by generating alternative ways of framing problems, thereby supporting paradigm shifts that enable more fundamental forms of innovation.

To support complex problem‑solving, the authors outline structured approaches including morphological analysis, analogical thinking, and benchmarking. Morphological analysis is particularly effective for problems involving multiple variables and stakeholders, as it systematically explores combinations that might otherwise be overlooked. Analogies and benchmarking extend the search for solutions beyond familiar domains, while AI accelerates these processes by identifying patterns, generating combinations, and visualizing implications across large datasets.

Interdisciplinary collaboration and open innovation further expand creative capacity. By integrating insights from different fields and engaging contributors beyond organizational boundaries, teams can access perspectives that would otherwise remain unavailable. AI can support this work by synthesizing knowledge across domains or simulating expert viewpoints, reinforcing the authors’ argument that creativity benefits from structured diversity rather than isolated insight.

In its later chapters, the book turns to trend analysis, experimentation, and iteration. AI’s ability to detect emerging patterns and intersections among trends can inform strategic foresight, though the authors caution against uncritical reliance on algorithmic outputs. Ultimately, creative ideas must be tested, refined, and validated through experimentation. Tools such as digital twins illustrate how AI can accelerate this process by enabling low‑risk simulation before real‑world implementation.

The book concludes by emphasizing curiosity and imagination as the foundations of creativity. Leaders play a critical role in fostering environments that support both directed inquiry and open exploration. Emerging technologies, including immersive environments, further extend the contexts in which creativity can occur, with AI serving as an integrative layer across these tools. Rather than prescribing a single method, the authors encourage readers to assemble a personalized toolkit of creative strategies, selected and refined through experimentation. Creativity, they argue, is sustained not by novelty alone, but by disciplined practice, reflection, and persistence over time.

 This is a summary of a book titled “Creativity in the Age of AI: Toolkits for the Modern Mind” written by Jerry (Yoram) Wind, Mukul Pandya and Deborah Yao and published by De Gruyter in 2025. Recommendation. This book examines creativity as a disciplined capability rather than a sporadic or innate talent, situating it within the contemporary context of artificial intelligence. The authors contend that creativity has become a central requirement for organizational effectiveness and long‑term competitiveness, particularly as AI technologies alter how problems are framed, explored, and solved. Their central claim is not that AI supplants human creativity, but that it can extend and reinforce it when integrated into established cognitive, organizational, and analytical frameworks.

The book begins by establishing creativity as an essential component of business performance. Empirical research demonstrates a strong relationship between creative capability and outcomes such as revenue growth and market share, yet many organizations struggle to translate creativity into systematic practice. This gap, the authors argue, stems from persistent misconceptions: creativity is often treated as an unpredictable spark rather than as a process that can be deliberately cultivated. Drawing on longstanding research, the authors emphasize that creativity requires both novelty and usefulness, and that ideas only become creative when they are developed into practical and effective solutions.

To clarify how creativity functions, the authors revisit foundational models that remain relevant in the age of AI. Graham Wallas’s four‑stage framework—preparation, incubation, illumination, and verification—illustrates creativity as a progression from problem definition to refinement and implementation. Teresa Amabile’s componential theory further expands this view by identifying the interacting elements that support creativity: domain‑specific expertise, cognitive processes that enable creative thinking, intrinsic motivation, and an environment that encourages exploration and risk‑taking. Together, these models reinforce the authors’ view that creativity is the result of sustained effort shaped by both individual and contextual factors.

Advances in neuroscience provide additional support for this perspective. Research shows that creativity is supported by the interaction of three neural networks: the default mode network, which generates ideas; the executive control network, which evaluates and refines them; and the salience network, which mediates between exploration and judgment. Creativity depends on maintaining balance among these systems, a balance influenced by factors such as cognitive flexibility, intrinsic motivation, and psychological safety. Environmental conditions also matter. Spaces characterized by coherence, fascination, or comfort can support different phases of creative work, suggesting that creativity is shaped not only by mental processes but also by physical and social contexts.

Within this human-centered framework, AI is introduced as a complementary resource rather than a disruptive replacement. The authors position AI as an assistant and collaborator that can support creativity across execution, idea generation, and evaluation. By handling routine tasks, generating unconventional combinations, and providing analytical feedback, AI can expand the range of possibilities considered while allowing humans to focus on judgment and meaning. The example of Airbus’s use of generative design illustrates this dynamic: AI explored vast design spaces beyond human capacity, while engineers defined constraints and evaluated outcomes. The result was a solution that combined biological inspiration with engineering requirements, demonstrating how AI can augment, rather than diminish, human creative agency.

The authors also address the organizational challenges associated with adopting AI for creative work. Resistance to change, fear of failure, and limited resources can all impede progress. Rather than dismissing these concerns, the authors recommend examining their underlying causes and addressing them explicitly. Techniques such as pre‑mortem analysis can reduce uncertainty, while reframing obstacles as opportunities for reconsideration can help organizations move beyond entrenched habits. Creativity, in this view, requires not only tools but also cultural conditions that tolerate experimentation and learning.

A significant portion of the book is devoted to the role of mental models in shaping creative outcomes. Unexamined assumptions can constrain perception and limit the range of solutions considered. The authors argue that creativity depends on the continual reassessment of these models through techniques such as assumption reversal, analogical reasoning, and exposure to diverse perspectives. General‑purpose AI tools can assist by making implicit assumptions visible and by generating alternative ways of framing problems, thereby supporting paradigm shifts that enable more fundamental forms of innovation.

To support complex problem‑solving, the authors outline structured approaches including morphological analysis, analogical thinking, and benchmarking. Morphological analysis is particularly effective for problems involving multiple variables and stakeholders, as it systematically explores combinations that might otherwise be overlooked. Analogies and benchmarking extend the search for solutions beyond familiar domains, while AI accelerates these processes by identifying patterns, generating combinations, and visualizing implications across large datasets.

Interdisciplinary collaboration and open innovation further expand creative capacity. By integrating insights from different fields and engaging contributors beyond organizational boundaries, teams can access perspectives that would otherwise remain unavailable. AI can support this work by synthesizing knowledge across domains or simulating expert viewpoints, reinforcing the authors’ argument that creativity benefits from structured diversity rather than isolated insight.

In its later chapters, the book turns to trend analysis, experimentation, and iteration. AI’s ability to detect emerging patterns and intersections among trends can inform strategic foresight, though the authors caution against uncritical reliance on algorithmic outputs. Ultimately, creative ideas must be tested, refined, and validated through experimentation. Tools such as digital twins illustrate how AI can accelerate this process by enabling low‑risk simulation before real‑world implementation.

The book concludes by emphasizing curiosity and imagination as the foundations of creativity. Leaders play a critical role in fostering environments that support both directed inquiry and open exploration. Emerging technologies, including immersive environments, further extend the contexts in which creativity can occur, with AI serving as an integrative layer across these tools. Rather than prescribing a single method, the authors encourage readers to assemble a personalized toolkit of creative strategies, selected and refined through experimentation. Creativity, they argue, is sustained not by novelty alone, but by disciplined practice, reflection, and persistence over time.

 Over the next three months, our work on releasing drone video sensing analytics framework can resume with a sequence that begins with re‑establishing customer contact, stabilizing the technical core, and preparing for the upcoming industry events.

The first month focuses on restarting the structured conversations with early adopters in construction, utilities, and public‑safety programs. These conversations are necessary to validate the spatio‑temporal cataloging approach and to rebuild the cost‑effectiveness narrative. This period also includes bringing the ezbenchmark extension back to a stable point, ensuring the TPC‑H‑inspired queries, agentic retrieval components, and reasoning‑model evaluation behave consistently. As this stabilizes, the paper submissions can resume, organized around the same three themes described in the earlier plan: real‑time drone‑to‑cloud feedback loops, temporal and spatial cataloging for scene understanding, and the economics of reasoning‑augmented pipelines. With this foundation in place, I will produce a short technical article or vision deck to reintroduce the benchmarking philosophy and the importance of reproducibility in drone analytics. This aligns with the original intent that the first month should “ground the work in real user needs and enhance the solution proposed”

The second month shifts toward outward‑facing activity because several relevant events occur in this window. Early March includes the Japan UAS and C‑UAS Defense Industry Day, followed by the New England Next Generation Aviation Summit on March 19. AUVSI XPONENTIAL Europe takes place March 24–26, offering the first major venue for re‑engaging with the broader autonomy and drone‑analytics community. These events provide opportunities to submit abstracts, attend sessions, or request poster or panel slots. They also create a natural lead‑in to XPONENTIAL 2026 in Detroit in mid‑May, which is the most strategically important event on the horizon. During this same month, one or two small pilot engagements can be restarted with friendly customers. These pilots should demonstrate long‑path object tracking, temporal queries over cataloged scenes, and the efficiency gains of structured prompting. The data collected will strengthen both the publication narrative and the release announcement. By the end of this month, the framework should again be approaching a clean public‑facing shape, with a stable API surface, reproducible scripts, and documentation that makes ingestion, cataloging, and querying straightforward.

The third month becomes the release window. With the technical core stable and the narrative complete, the framework can be published on GitHub with a polished README, example workflows, and benchmark results, just as the original plan envisioned. A companion website and whitepaper can summarize the cost‑model analysis and explain the value of agentic retrieval in a way that is accessible to both researchers and practitioners. This period aligns with XPONENTIAL Detroit, which becomes the anchor for a coordinated announcement across LinkedIn, ResearchGate, and the drone‑analytics communities you follow. A virtual workshop can accompany the release, demonstrating real‑time ingestion, temporal and spatial cataloging, LLM‑as‑a‑judge evaluation, and cost‑optimized reasoning workflows. If early adopters are willing to share their pilot experience, even informally, their participation adds credibility. After the release, attention can shift back to the research community through paper submissions and guest talks to university labs or robotics groups. Engagement with open‑source UAV dataset communities can begin, positioning the benchmark as a complementary tool and helping build the ecosystem around the framework.

 Most drones don’t have radars. They merely have positions that they change based on fully autonomous decisions or provided by a controller. In the former case, the waypoints and trajectory determine the flight path, and each drone independently tries to minimize the errors in deviations from the flight path while aligning its path using the least squares method. The selection of waypoints and the velocity and ETA at each waypoint is determined for each unit in a UAV swarm with ability to make up delays or adjust ETAs using conditional probability between past and next waypoint while choosing a path of least resistance or conflict between the two. Usually, a formation, say matrix, already spreads out the units, and its center of mass is used to calculate the progress on the flight path for the formation. This article discusses a novel approach to minimize conflicts and adhere to the path of least resistance.

For example, to transform between an “Abreast” and a “Diamond” formation, any technique must demonstrate efficiency in minimizing transformation distance and maintaining formation coherence. Similarly, to transform matrix formation to flying linearly under a bridge between its piers, any technique must demonstrate a consensus based on pre-determined order.

The approach included here defines a drone formation state with six parameters: time, 3D positions, yaw angle (heading), and velocity. For a formation to be considered coherent, all drones must share the same heading and speed while maintaining relative positions—essential for realistic aerial maneuvers.

The transformation itself consists of two steps: location assignment and path programming. First, to determine which drone should move to which position in the new formation, the Hungarian algorithm, a centralized optimization method is used or in its absence the information about the greatest common denominator for volume between two waypoints determines the number of multiple simultaneous paths to choose and the matrix model is used to assign the positions for the drones to the nearest path. If there is only one path and no centralized controller, the units use the Paxos algorithm for coming to a consensus on the linear order. This first step evaluates the cost of moving each drone to each new position by considering spatial displacement, heading change, and velocity difference. This ensures that the assignment minimizes overall disruption and maneuvering effort.

Second, each drone calculates its own flight path to the newly assigned position using a Dubins path model, which generates the shortest possible route under a minimum turning radius constraint—a requirement for fixed-wing drones that can’t make sharp turns or hover. Positions alone do not guarantee compliance, and the velocity adjustments for each unit must also be layered over the transition. The adjustment of velocities follows a Bayesian conditional probability along the associated path for the unit. This involves computing acceleration and deceleration phases to fine-tune the duration and dynamics of the transition with error corrections against deviations.

Overall, this provides a cohesive framework for in-flight drone formation reconfiguration that balances centralized planning with distributed execution. By coding the physical constraints and states for each unit and classifying the adherence, outliers can be handled by rotating them with other units for a smooth overall progression for the formation and overcoming environmental factors such as turbulence with error corrections.

Lastly, a simple Hungarian algorithm application demonstration with sample code to determine the position allocation in formation transformation.

#! /usr/bin/python

# pip install hungarian-algorithm

from hungarian_algorithm import algorithm

import numpy as np

# Source: drones in a 3×3 grid on Z=0 plane

source_positions = [

    (x, y, 0)

    for y in range(3)

    for x in range(3)

]

# Target: drones in a single horizontal line (linear flight path), spaced 10 units apart

target_positions = [

    (i * 10, 0, 0) for i in range(9)

]

# Compute cost matrix (Euclidean distance)

cost_matrix = [

    [

        np.linalg.norm(np.array(src) - np.array(dst))

        for dst in target_positions

    ]

    for src in source_positions

]

# Run Hungarian Algorithm to get minimum-cost assignment

assignment = algorithm.find_matching(cost_matrix, matching_type='min')

# Report matched pairs

for src_idx, dst_idx in enumerate(assignment):

    print(f"Drone {src_idx} → Target Position {dst_idx}: {target_positions[dst_idx]}")

 This is a summary of the book titled “Applying AI in Learning and Development: From Platforms to Performance” written by Josh Cavalier and published by ATD (Association for Talent Development) in 2025. This book examines how learning and development (L\&D) professionals can use artificial intelligence thoughtfully to improve both learning efficiency and organizational performance. Rather than presenting AI as a replacement for human expertise, the book positions it as a partner that can handle routine, data-intensive tasks while allowing L\&D professionals to focus on strategy, analysis, and design.

Cavalier begins by showing how AI can streamline common instructional design activities. Tasks such as transcribing interviews, summarizing discussions, or generating draft materials—once time-consuming—can be completed quickly with AI support. As these efficiencies increase, the role of the L\&D professional evolves. The book introduces the idea of the human–machine performance analyst (HMPA), a role in which practitioners use judgment, contextual knowledge, and empathy to interpret data and shape learning interventions, while AI supports content creation and analysis. An example illustrates this shift: when compliance incidents continued despite high course completion rates, an L\&D professional used AI-generated data as a starting point but relied on interviews and observation to identify the real issue—irrelevant training. Redesigning the program into role-specific scenarios led to a measurable reduction in incidents.

Throughout the book, he emphasizes that the core skills of L\&D—understanding how people learn, connecting learning to performance, and aligning learning with business outcomes—remain unchanged. What has changed is the set of tools available and the scope of influence L\&D can have across an organization. He encourages teams to begin experimenting with AI in small, low-risk ways, such as using meeting assistants to capture action items or deploying chatbots to answer frequently asked learner questions. Progress should be tracked, lessons documented, and experimentation treated as part of normal professional growth rather than a one-time initiative.

A significant portion of the book focuses on assessing an organization’s current relationship with AI. He outlines several common patterns, ranging from informal individual experimentation to full organizational integration. In some organizations, employees use external AI tools without guidance, increasing the risk of data exposure. Others hesitate to act at all, stalled by concerns about privacy, bias, or regulation. Still others implement AI unevenly, creating silos where some departments benefit while others are left behind. The most mature organizations, by contrast, provide approved tools, clear policies, and role-specific training that allow AI to be used consistently and responsibly. Understanding where an organization falls along this spectrum helps L\&D leaders determine realistic next steps.

From there, the book argues that successful AI adoption depends less on choosing a particular tool and more on establishing a strong foundation. AI initiatives should be explicitly tied to business goals such as faster onboarding, improved compliance, or better customer service, with clear explanations of how time or costs will be saved. Small pilots and case studies can demonstrate value and reduce resistance, especially when results are communicated through concrete comparisons rather than abstract claims.

He places strong emphasis on governance. While many L\&D professionals already experiment with AI, far fewer feel confident about using it ethically. An effective AI policy, he argues, must address data privacy, security, regulatory compliance, and bias. Policies should specify which tools are approved, what information can be shared with them, and where human review is required. The book uses the well-known example of Amazon’s abandoned résumé-screening system to illustrate how biased training data can produce discriminatory outcomes. To mitigate these risks, he recommends close collaboration with legal, HR, and cybersecurity teams, as well as processes that allow learners and employees to flag AI-generated content for review.

When it comes to technology selection, the book encourages L\&D leaders to advocate for platforms that support both learning and broader business needs. Many organizations are moving away from standalone learning management systems toward integrated human capital management platforms that combine learning, skills tracking, performance management, and workforce planning. He suggests defining what the organization wants AI to accomplish over the next six to twelve months and evaluating vendors against practical criteria such as transparency, system integration, usability, analytics, scalability, support, security, and return on investment. The goal is not to adopt the most advanced system available, but to choose the one that fits the organization’s context and constraints.

The book also provides detailed guidance on working effectively with generative AI. Cavalier stresses that output quality depends heavily on prompt quality. Clear instructions, explicit constraints, and well-defined criteria produce more useful results than vague requests. He recommends treating prompts as reusable assets by developing templates and maintaining a shared prompt library that documents use cases, tested models, and variations. Chaining prompts within a single session—moving from objectives to outlines to scripts, for example, can also improve coherence. Despite these efficiencies, the book repeatedly underscores the importance of human oversight to ensure accuracy, relevance, and alignment with learning goals.

In its final section, the book explores the use of AI agents to personalize learning at scale. Unlike traditional automated systems, these agents can reason, adapt, and make recommendations based on learner data, such as skill gaps, goals, and performance trends. Examples show how personalized recommendations can increase engagement and motivation. However, he is careful to frame AI agents as collaborators rather than autonomous decision-makers. He advocates for models in which AI proposes learning paths or resources, while human coaches or managers remain involved in reflection and decision-making. Implementing these systems requires careful attention to data quality, accessibility, integration with existing platforms, and iterative testing with pilot groups.

Overall, Applying AI in Learning and Development presents AI not as a disruptive force to be feared or a shortcut to be exploited, but as a tool that amplifies the strategic role of L&D. By combining experimentation with governance, efficiency with human judgment, and technology with organizational context, he argues that L&D professionals can use AI to deliver learning that is both more personalized and more closely tied to real performance outcomes.

 3756. Concatenate Non-Zero Digits and Multiply by Sum II

You are given a string s of length m consisting of digits. You are also given a 2D integer array queries, where queries[i] = [li, ri].

For each queries[i], extract the substring s[li..ri]. Then, perform the following:

Form a new integer x by concatenating all the non-zero digits from the substring in their original order. If there are no non-zero digits, x = 0.

Let sum be the sum of digits in x. The answer is x * sum.

Return an array of integers answer where answer[i] is the answer to the ith query.

Since the answers may be very large, return them modulo 109 + 7.

Example 1:

Input: s = "10203004", queries = [[0,7],[1,3],[4,6]]

Output: [12340, 4, 9]

Explanation:

s[0..7] = "10203004"

x = 1234

sum = 1 + 2 + 3 + 4 = 10

Therefore, answer is 1234 * 10 = 12340.

s[1..3] = "020"

x = 2

sum = 2

Therefore, the answer is 2 * 2 = 4.

s[4..6] = "300"

x = 3

sum = 3

Therefore, the answer is 3 * 3 = 9.

Example 2:

Input: s = "1000", queries = [[0,3],[1,1]]

Output: [1, 0]

Explanation:

s[0..3] = "1000"

x = 1

sum = 1

Therefore, the answer is 1 * 1 = 1.

s[1..1] = "0"

x = 0

sum = 0

Therefore, the answer is 0 * 0 = 0.

Example 3:

Input: s = "9876543210", queries = [[0,9]]

Output: [444444137]

Explanation:

s[0..9] = "9876543210"

x = 987654321

sum = 9 + 8 + 7 + 6 + 5 + 4 + 3 + 2 + 1 = 45

Therefore, the answer is 987654321 * 45 = 44444444445.

We return 44444444445 modulo (109 + 7) = 444444137.

Constraints:

1 <= m == s.length <= 105

s consists of digits only.

1 <= queries.length <= 105

queries[i] = [li, ri]

0 <= li <= ri < m

 Solution:

class Solution {

    public int[] sumAndMultiply(String s, int[][] queries) {

        int[] answers = new int[queries.length];

        for (int i = 0; i < queries.length; i++){

            long x = 0;

            long sum = 0;

            String sub = s.substring(queries[i][0], queries[i][1]+1);

            for (int j = 0; j < sub.length(); j++) {

                int numericValue = Character.getNumericValue(sub.charAt(j));

                if (numericValue != 0) {

                    x = x * 10 + numericValue;

                    sum += numericValue;

                }

            }

            answers[i] = (int) ((x * sum) % (Math.pow(10, 9) + 7));

        }

        return answers;

    }

}

 This is a summary of a book titled “The Datapreneurs: The Promise of AI and the Creators Building Our Future” written by Steve Hamm and Bob Muglia and published by Peakpoint Press, 2023. This book examines how artificial intelligence and data-driven systems are reshaping the economy, technology, and society. The authors argue that the world is entering a period in which intelligence, labor, and energy—the three foundational forces of the modern economy—are all becoming cheaper due to technological advances. Artificial intelligence, particularly the development of artificial general intelligence and the possibility of artificial superintelligence, has the potential to add intelligence to nearly every device and application. At the same time, progress in renewable and advanced energy technologies may reduce the cost of electricity, while robotics could significantly lower the cost of certain kinds of labor. Together, these shifts point toward a profound economic transformation.

The authors suggest that within the next decade, many of the AI assistants people interact with daily could surpass the level of median human intelligence. As these systems evolve through successive generations, they may become capable of artificial superintelligence, potentially exceeding the combined intellectual capacity of humanity. This development could trigger what has often been described as a technological singularity, a moment when technological progress accelerates beyond human prediction or control. Such a shift could compress centuries of scientific and economic advancement into a much shorter time span, creating opportunities to address persistent global challenges such as climate change, disease, and poverty. However, the authors emphasize that these outcomes are not guaranteed and depend heavily on how humans choose to guide and govern intelligent machines.

The authors delve into need for ethics and values to shape the relationship between humans and machines. They contrast optimistic visions of a future characterized by abundance and ease with darker, more dystopian possibilities in which powerful machines generate fear or inequality. To avoid harmful outcomes, they argue for the creation of a new social contract that defines how intelligent systems should behave once they exceed human capabilities. Because advanced machines will increasingly make decisions and take actions independently, the values embedded in their design will play a decisive role in shaping their impact. Establishing ethical frameworks is therefore not an abstract concern but a practical necessity for long-term human and machine collaboration.

The book places current developments in artificial intelligence within a longer historical context by tracing the evolution of data management technologies. Relational databases are presented as a foundational breakthrough that made modern data-driven computing possible. Earlier systems relied on rigid hierarchical or network-based structures that were difficult to update and scale. The relational model, developed by IBM researcher Ted Codd in 1970, introduced a more flexible way to organize data, allowing relationships to be defined mathematically rather than hard-coded into applications. The introduction of SQL and the commercialization of relational databases by companies such as IBM, Oracle, and Sybase helped make data more accessible and adaptable for organizations of all sizes.

Microsoft’s role in expanding access to data management is highlighted as a key moment in the democratization of computing. The company’s emphasis on making information readily available, combined with the release of more affordable and user-friendly database systems such as SQL Server 7.0, lowered barriers for smaller businesses that previously lacked access to enterprise-level data tools. By reducing costs and simplifying maintenance, Microsoft helped bring advanced data processing capabilities beyond large corporations and into the broader economy.

As data volumes grew, the book explains, new infrastructure became necessary to support machine learning and AI systems. Cloud-based data platforms and pipelines now allow organizations to store, process, and move massive amounts of structured and unstructured data. These pipelines function as connective tissue, transferring data into centralized repositories where it can be used to train AI systems. In this framework, future AI assistants will increasingly learn from data warehouses and data lakes, drawing insights from continuous streams of information rather than static datasets.

The authors also describe the emergence of data applications, which differ from traditional software by responding directly to changes in data rather than user commands. Powered by relational knowledge graphs and predictive models, these systems can automate routine decisions and actions. As a result, many administrative tasks may be handled by machines, allowing people to focus on analysis, strategy, and creative problem-solving. This shift extends to autonomous systems such as drones and self-driving vehicles, which require databases capable of synchronizing data rapidly across networks to ensure safety and coordination.

The book further explores the importance of programming languages in the evolving data ecosystem, particularly the rise of Julia. Designed to address inefficiencies in data science workflows, Julia enables high-performance computing without requiring developers to rewrite code in lower-level languages. Its support for automatic differentiation makes it well suited for building predictive models and neural networks, and it is already being used in fields ranging from finance to climate science.

Finally, the authors turn to foundation models, large-scale AI systems trained on vast datasets that exhibit emergent capabilities. These models can be adapted for a wide range of tasks, from writing text to generating images and assisting with software development. Powered by neural networks, such systems can sense, learn, reason, plan, adapt, and act with increasing autonomy. As these capabilities advance, the authors argue that computer scientists and society as a whole must prepare for a future in which machines generate long-term plans and predictions. The book concludes that while superintelligent systems hold enormous promise, their impact will ultimately depend on the values and responsibilities humans choose to embed within them.

 While operational and analytical data gets rigorous treatment in terms of the pillars of good architecture such as purview, privacy, security, governance, encryption at rest and in transit, aging, tiering and such others, DevOps tasks comprising Extract-Transform-Load, backup/restore and such others, is often brushed aside but never eliminated for the convenience they provide. This is inclusive of the vast vector stores that have now become central to building contextual copilots in many scenarios.

One of the tools to empower access of data for purposes other than transactional or analytics is the ability to connect to it with a client native to the store where the data resides. Even if the store is in the cloud, data plane access is usually independent of the control plane command-line interfaces. This calls for a creating a custom image that can be used on any compute to spin up a container with ability to access the vectors. For example, this Dockerfile installs clients:

FROM python:3.13-latest-dev

USER root

RUN apt-get update && \

    apt-get install -y ksh \

    ldap-utils \

    mysql-client \

    vim \

    wget \

    curl \

    libdbd-mysql-perl \

    libcurl4-openssl-dev \

    rsync \

    libev4 \

    tzdata \

    jq \

    pigz \

    python3-minimal \

    python3-pip && \

    apt-get clean && \

    rm -rf /var/lib/apt/lists/* && \

    pip3 install s3cmd

RUN apk add --no-cache mariadb mariadb-client

RUN pip install azure-storage-blob requests

RUN pip install requests

WORKDIR /app

COPY custom_installs.py .

RUN mysqldump --version

RUN mysql --version

ENTRYPOINT ["python", "custom_installs.py"]

 This is a summary of the book titled “Technology for Good: How Nonprofit Leaders Are Using Software and Data to Solve Our Most Pressing Social Problems” written by Jim Fruchterman and published by MIT Press, 2025. This book piques my interest because bad ideas need to be abandoned fast and both startups and non-profits struggle with that until it becomes critical. In this book, the author explores why high-growth, profit-driven start-ups can and nonprofit technology ventures cannot. While the popular imagination tends to focus on for-profit start-ups capable of viral success and massive wealth creation, Fruchterman argues that nonprofit tech start-ups play an equally important role in shaping the future, particularly when it comes to addressing entrenched social problems. Drawing on his experience as a social entrepreneur, he offers a practical guide to building social enterprises, noting that while nonprofit and for-profit start-ups face similar challenges in developing ideas and raising capital, nonprofits benefit from a crucial advantage. Because they are not beholden to investors seeking financial returns, nonprofit founders have greater freedom to prioritize impact over profit.

Nonprofit organizations are chronically behind the technology curve. Tight budgets and donor expectations often leave charities and public agencies relying on outdated hardware and software, sometimes lagging a decade or more behind current standards. Although technology is essential to modern organizational effectiveness, donors frequently view technology spending as overhead rather than as a core part of the mission. Fruchterman challenges this mindset and emphasizes that the most effective way for nonprofits to modernize is often by adapting widely used, standard platforms rather than attempting to build custom solutions from scratch. Tools such as Microsoft Office or Slack can meet many needs, and large technology companies frequently offer discounted pricing to nonprofits, often coordinated through organizations like TechSoup Global. While custom software development is sometimes necessary, it is usually more cost-effective to purchase existing solutions, provided the organization has enough technical expertise to manage vendor relationships and protect its interests. In rare cases, nonprofits even form specifically to create technology that the commercial market has failed to address.

Fruchterman is particularly critical of the nonprofit sector’s tendency to incubate ill-fated technological innovations. Unlike the for-profit world, where start-ups are encouraged to test ideas quickly, gather feedback, and abandon bad concepts early, nonprofit leaders often cling to flawed ideas for too long. One common mistake is the assumption that every organization needs a mobile app simply because apps are ubiquitous in everyday life. In reality, most users do not want more apps, and many nonprofit apps fail to gain traction. The author also cautions against rushing into experimental or heavily hyped technologies. Blockchain, for example, attracted significant attention after the success of Bitcoin, leading many donors and nonprofits to assume it could be easily repurposed for social good. In practice, blockchain initiatives have often failed to deliver meaningful benefits, as illustrated by costly implementations that outweighed their promised savings. Fruchterman urges social leaders to remain skeptical and clear-eyed, especially when technologies are promoted by those more focused on ideology than sound technical design.

Despite these pitfalls, the book makes a strong case that thoughtfully deployed technology can dramatically increase the social sector’s impact. While for-profit companies often aim to eliminate human interaction through automation, nonprofits tend to emphasize person-to-person relationships. Fruchterman argues that technology should not replace human connection in the social sector, but rather support it, particularly by improving efficiency for frontline workers. When those closest to the people being served can work more effectively, the organization’s overall impact is amplified. He also highlights the potential of delivering well-designed tools directly to communities themselves.

One illustrative example is Medic, a social organization that builds tools specifically for community health workers. By replacing paper forms with digital data and linking frontline workers to local health systems, Medic created an app that succeeded precisely because it was narrowly targeted and deeply practical. Although most nonprofit apps add little value, Medic’s tool stands out because it was designed for a clearly defined audience and addressed real operational needs. The result was improved outcomes in areas such as maternal health, disease treatment, and vaccination tracking.

Fruchterman also challenges conventional nonprofit strategic planning. He argues that long-term strategic plans are often too rigid to survive in a rapidly changing world, a lesson underscored by the COVID-19 pandemic, which rendered many carefully crafted plans irrelevant almost overnight. Instead of producing static documents, nonprofits should adopt a more agile approach to strategy that remains grounded in mission while allowing for rapid adaptation. This means focusing on the organization’s core objectives—the “what”—rather than locking into specific tactics—the “how.” By collecting real-time data and learning continuously from results, nonprofits can test assumptions, adjust programs, and respond more effectively to changing conditions.

The book devotes significant attention to artificial intelligence, emphasizing both its promise and its limitations. Fruchterman stresses that AI systems are only as good as the data used to train them, and that bias is an unavoidable risk when datasets are incomplete or unrepresentative. Because many AI tools are developed primarily in English and rely on mainstream data sources, they often overlook the poor and underserved populations that nonprofits aim to support. The author illustrates this problem with examples of biased facial recognition systems that perform poorly on women and people of color due to skewed training data. Such cases underscore the importance of diverse development teams and careful scrutiny when deploying AI in social contexts.

Another key distinction Fruchterman draws is between the goals of nonprofit and for-profit start-ups. While commercial tech ventures are often driven by the promise of wealth, nonprofit start-ups exist to serve people who cannot pay for services. As a result, financial success is defined not by profits but by impact and sustainability. Although the motivations differ, the basic phases of launching a start-up are similar, beginning with exploration and user research, followed by development, growth, and eventual maturity. Throughout these stages, nonprofit founders must be disciplined about testing ideas, releasing imperfect products, and learning from feedback.

Funding and talent emerge as persistent challenges for nonprofit tech start-ups. Fruchterman estimates that early-stage funding typically ranges from modest six-figure sums to around a million dollars for more ambitious projects, with founders often contributing unpaid labor in the beginning. Philanthropic foundations, fellowship programs, accelerators, government agencies, and corporate social good initiatives all play important roles in supporting these ventures. Unlike for-profit start-ups, nonprofits aim simply to break even while maximizing the number of people they help. Although nonprofits cannot compete with the salaries offered by commercial tech firms, they can attract professionals motivated by purpose rather than profit, particularly when expectations around compensation are addressed transparently from the outset.

Fruchterman argues that social entrepreneurs should prioritize empowering communities and individuals rather than imposing top-down solutions. Access to healthcare, education, capital, and inclusion can transform lives, and technology can be a powerful enabler when used responsibly. He encourages nonprofit leaders to embrace data collection and cloud-based tools while remaining transparent about how data is used and firmly committed to protecting it from exploitation. The book closes with a call to use AI and other emerging technologies for good, capturing efficiency gains without surrendering human judgment or ethical responsibility. Fruchterman has a long career in social entrepreneurship and open-source development that gives authenticity to his message that when technology is guided by mission, humility and respect for the people it serves, it can become a powerful force for positive social change.

 While operational and analytical data gets rigorous treatment in terms of the pillars of good architecture such as purview, privacy, security, governance, encryption at rest and in transit, aging, tiering and such others, DevOps tasks comprising Extract-Transform-Load, backup/restore and such others, is often brushed aside but never eliminated for the convenience they provide. This is inclusive of the vast vector stores that have now become central to building contextual copilots in many scenarios.

One of the tools to empower access of data for purposes other than transactional or analytics is the ability to connect to it with a client native to the store where the data resides. Even if the store is in the cloud, data plane access is usually independent of the control plane command-line interfaces. This calls for a creating a custom image that can be used on any compute to spin up a container with ability to access the vectors. For example, this Dockerfile installs clients:

FROM python:3.13-latest-dev

USER root

RUN apt-get update && \

    apt-get install -y ksh \

    ldap-utils \

    mysql-client \

    vim \

    wget \

    curl \

    libdbd-mysql-perl \

    libcurl4-openssl-dev \

    rsync \

    libev4 \

    tzdata \

    jq \

    pigz \

    python3-minimal \

    python3-pip && \

    apt-get clean && \

    rm -rf /var/lib/apt/lists/* && \

    pip3 install s3cmd

RUN apk add --no-cache mariadb mariadb-client

RUN pip install azure-storage-blob requests

RUN pip install requests

WORKDIR /app

COPY custom_installs.py .

RUN mysqldump --version

RUN mysql --version

ENTRYPOINT ["python", "custom_installs.py"]

 

This is a summary of the book titled “How the Future Works: Leading Flexible Teams To Do The Best Work of Their Lives” written by Brian Elliott, Sheela Subramanian and Helen Kupp and published by Wiley, 2022. In this book, the authors examine one of the most profound transformations in modern business: the rapid and irreversible shift toward flexible work. Written in the aftermath of the COVID-19 pandemic, the book argues that what began as an emergency response has evolved into a durable and preferable way of working—one that challenges long-held assumptions about productivity, leadership, and the role of the traditional office.

Before the pandemic, flexible work arrangements were rare and often reserved for elite performers. Most organizations relied on physical offices, fixed schedules, and direct supervision as the foundation of productivity. Many leaders believed that innovation depended on employees sharing the same space, learning through proximity, and being visibly present. The idea of managing a distributed workforce seemed risky, if not impossible. Yet when offices abruptly closed in 2019, companies had no choice but to test those assumptions at scale.

What followed surprised many executives. Productivity did not collapse; in many cases, it increased. Employees reported greater autonomy, improved focus, and stronger work–life balance. Creativity and innovation continued, and in some organizations even flourished. As the authors note, flexibility turned into a powerful advantage in recruiting and retaining talent, particularly in a highly competitive labor market. The authors conclude that a full return to rigid, office-centered work is both unlikely and undesirable.

Central to the book’s argument is the idea that traditional measures of productivity were flawed long before remote work became common. Managers once relied on visible activity—attendance, desk time, and “management by walking around”—as proxies for performance. These methods fail in distributed environments and, more importantly, never truly measured the quality or impact of work in the first place. Seeing employees at their desks does not reveal whether they are engaged, effective, or producing meaningful outcomes.

To help organizations adapt, the authors outline seven interrelated steps for retrofitting companies for the future of work. The first is to operate according to a clear and shared set of principles. Because flexibility introduces complexity and uncertainty, principles act as a compass for decision-making. Rather than imposing uniform rules, leaders should prioritize team-level autonomy, recognize that different functions require different approaches, and adopt a digital-first mindset that treats remote participation as the default rather than the exception.

Principles alone, however, are not enough. Organizations must also establish behavioral guidelines that translate values into everyday practices. These “guardrails” ensure fairness and prevent the emergence of “faux flexibility,” where policies appear progressive but still constrain employee autonomy. Examples such as Slack’s “one dials in, all dial in” rule demonstrate how simple norms can reinforce inclusion and equity across hybrid teams.

A defining theme of the book is collaboration rather than control. The authors caution against top-down mandates and instead encourage leaders to co-create flexible work policies with employees. Teams that are already working effectively should be studied and learned from, and flexibility should be formalized through team-level agreements that clarify expectations around schedules, communication, accountability, and relationships. This participatory approach builds trust and ensures that flexibility works for both individuals and the organization.

Because no universal blueprint exists, experimentation is essential. Leaders must accept uncertainty, support pilot programs, and view trial and error not as failure but as learning. Over time, patterns emerge that reveal what truly supports performance and well-being. The authors emphasize that there is no perfect data point or benchmark—only continuous improvement guided by experience and feedback.

The book also challenges the belief that culture depends on physical proximity. While companies once invested heavily in office campuses, the authors argue that connection and belonging can be cultivated virtually—and sometimes more inclusively than before. Research cited in the book links flexibility to stronger feelings of belonging, higher job satisfaction, and improved well-being, undermining the assumption that creativity depends on shared physical space.

Leadership, however, must evolve. The shift to flexible work has exposed weaknesses in managers who rely on control rather than trust. The authors advocate developing managers as coaches—leaders who communicate clearly, show empathy, and focus on outcomes instead of activity. Training initiatives like Slack’s “Base Camp” illustrate how organizations can intentionally build these capabilities.

The authors contrast two management paths: the “doom loop” of constant surveillance and the “boom loop” of trust and accountability. Excessive monitoring erodes morale, increases anxiety, and drives attrition, while goal-based management fosters engagement and performance. Tools such as the RACI matrix help organizations track progress without resorting to intrusive oversight, reinforcing the principle that results—not hours—matter most.

Flexibility is not a temporary accommodation but a defining feature of modern work. Employees want and need it, and organizations that embrace it thoughtfully gain a lasting competitive advantage. While flexibility is not a cure-all, the authors argue it is a decisive step toward healthier, more resilient, and more human workplaces when implemented with intention and trust.

#codingexercise:CodingExercise-02-12-2026