Foundation model companies are doing the same. OpenAI launched a dedicated Healthcare & Life Sciences vertical, complete with industry-specific sales teams and solutions engineers.

Harvey now positions itself as AI for legal and professional services, not just law firms. It serves corporate legal departments, court systems, and co-built a Tax AI model with PwC covering 25+ jurisdictions.

AI is moving faster than anyone predicted. When models change every 42 days, buyers can't assemble a best-of-breed stack.

a supercomputer scheduled to go live in 2026 was last month still a scaffolding yard in Essex

The OpenAI deal was part of a larger series of UK-US investments intended to 'mainline AI' into the British economy.

OpenAI shelves Stargate UK in blow to Britain's AI ambitions

The difference between AI and, say, looms, is that this has been broadcast to the entire globe, and it has been treated in a sort of self-conscious way

Meta is reportedly preparing to release its first AI models led by Alexandr Wang, with plans to open-source some versions while keeping its largest and most powerful systems closed.

One of the boldest ideas is a sovereign-style fund seeded by AI companies that would pay dividends to Americans, alongside robot taxes, stronger oversight systems, and containment plans for rogue autonomous AI.

OpenAI has published a 13-page policy paper arguing that AI may require a new social contract, with proposals that include taxing automated labor, creating a public wealth fund, expanding access to AI, and testing a four-day workweek.

The company added roughly $11 billion in annualized revenue in just over a month, equivalent to the combined ARR of Palantir, Anduril, and Databricks

Anthropic says its annual revenue run rate has climbed past $30 billion, overtaking OpenAI's reported $25 billion and marking one of the fastest ramps in AI.

Adobe just turned Firefly into a true all-in-one creative AI studio with its new Firefly AI Assistant that plans and executes multi-step workflows across apps like Photoshop, Premiere, Illustrator

Anthropic is expected to release Claude Opus 4.7 alongside a new AI-powered design tool for building websites and presentations

Google is expanding Gemini with a new agent system that can take a single goal and execute it across apps like Gmail, Drive, Calendar, and the web

The integration also connects to Upwork's AI agent Uma, which helps automate parts of the hiring and execution process once a project is underway

Meta is reportedly developing an AI version of Mark Zuckerberg that can interact with employees, trained on his voice, mannerisms, and internal thinking as part of the company's broader push into AI

Andon Labs deployed an AI agent called Luna into a physical boutique with a $100,000 budget, giving it full control to create, staff, and run the business as what may be the first real-world AI employer

Where training a language model took 167 minutes on eight GPUs in 2020, it now takes under four minutes on equivalent modern hardware.

From the time I began work on AI in 2010 to now, the amount of training data that goes into frontier AI models has grown by a staggering 1 trillion times—from roughly 10¹⁴ flops for early systems to over 10²⁶ flops for today's largest models.

A single refrigerator-size AI rack consumes 120 kilowatts, equivalent to 100 homes. But this hunger collides with another exponential: Solar costs have fallen by a factor of nearly 100 over 50 years; battery prices have dropped 97% over three decades.

Where training a language model took 167 minutes on eight GPUs in 2020, it now takes under four minutes on equivalent modern hardware. To put this in perspective: Moore's Law would predict only about a 5x improvement over this period. We saw 50x.

From the time I began work on AI in 2010 to now, the amount of training data that goes into frontier AI models has grown by a staggering 1 trillion times—from roughly 10¹⁴ flops for early systems to over 10²⁶ flops for today's largest models.

Forward Deployed Engineers tune Relvy for your stack

What excited me about Relvy was the potential to reduce our on-call burden

We improved Claude's RCA accuracy by 12pp on OpenRCA

M2.7 demonstrates excellent performance in real-world software engineering, including end-to-end project delivery, log analysis for bug hunting, code security, and machine learning tasks.

Reasoning-oriented models like OpenAI's o1 and GPT-5 show measurable gains over standard models—not only in logic and mathematics but also with interpreting user intent.

The same model can score above 90% on lower-demand tests and below 15% on more demanding ones, reflecting differences in task requirements rather than a change in capability.

Using these ability scores, the method predicts performance on new tasks with ~88% accuracy, including for models such as GPT-4o and Llama-3.1.

Over the past five months, they ran an extreme experiment: building and shipping an internal beta product with zero manually written code.

The top names you should know as a baseline, adjusted for 'what people are actually recommending'

we are NSFW-friendly so here goes…

roleplay/creative writing, the #2 usecase of LLMs

/r/localLlama (which has its own monthly top models thread)

Agents show only ~10% success on instances with PoCs longer than 100 bytes, which represent 65.7% of the benchmark

Out of all generated PoCs, 759 triggered crashes across 60 projects, and manual inspection confirmed 17 cases of incomplete patches spanning 15 projects

Tech valuations have compressed from 40x to 20x, and we are back at levels last seen before the AI boom began.

It also discovered a 16-year-old vulnerability in FFmpeg—which is used by innumerable pieces of software to encode and decode video—in a line of code that automated testing tools had hit five million times without ever catching the problem.

The window between a vulnerability being discovered and being exploited by an adversary has collapsed—what once took months now happens in minutes with AI.

Mythos Preview has already found thousands of high-severity vulnerabilities, including some in every major operating system and web browser.

In Washington, the AI policy discourse is sometimes framed as a 'race to AGI.' In contrast, in Beijing, the AI discourse is less abstract and focuses on economic and industrial applications that can support Beijing's overall economic objectives.

Like lean production, which extended mass production's dominance for decades through efficiency gains, AI doesn't mark computing's end but its maturation.

We built an automated scanning agent that systematically audited eight among the most prominent AI agent benchmarks — SWE-bench, WebArena, OSWorld, GAIA, Terminal-Bench, FieldWorkArena, and CAR-bench — and discovered that every single one can be exploited to achieve near-perfect scores without solving a single task.

Agent systems should be designed assuming prompt-injection and exfiltration attempts. Separating harness and compute helps keep credentials out of environments where model-generated code executes.

The updated Agents SDK made it production-viable for us to automate a critical clinical records workflow that previous approaches couldn't handle reliably enough.

For example, developers can give an agent a controlled workspace, explicit instructions, and the tools it needs to inspect evidence:

The shift started with agentic tools like Codex, which has grown more than 5X since the start of the year. This includes customers like GitHub, Nextdoor, Notion, and Wonderful that are building multi-agent systems that can execute engineering work end-to-end.

Codex just hit 3 million weekly active users, our APIs process more than 15 billion tokens per minute, and GPT‑5.4 is driving record engagement across agentic workflows.

Building on our consumer strength, enterprise now makes up more than 40% of our revenue, and is on track to reach parity with consumer by the end of 2026.

Meta also explicitly highlighted parallel multi-agent inference as a way to improve performance at similar latency

Gemma4-31B worked in an iterative-correction loop (with a long-term memory bank) for 2 hours to solve a problem that baseline GPT-5.4-Pro couldn't

Claude Mythos autonomously identified and exploited several significant vulnerabilities. Notably, it discovered a 27-year-old vulnerability in OpenBSD

Meta says its rebuilt pretraining stack can reach equivalent capability with >10× less compute than Llama 4 Maverick

The boundary between AI judgment and human judgment is explicit and written in code.

Kronos, a specialized pre-training framework for financial K-line data, outperforms existing models in forecasting and synthetic data generation through a unique tokenizer and autoregressive pre-training on a large dataset.

SkillClaw continuously aggregates trajectories generated during use and processes them with an autonomous evolver, which identifies recurring behavioral patterns and translates them into updates to the skill set by refining existing skills or extending them with new capabilities.

Add llms.txt metadata and root/package LICENSE files - Add website llms.txt support and move LICENSE to root - Fix llms.txt serving and restore package LICENSE

Add benchmark framework and release submission overview - Add benchmark runner with onlineMind2Web benchmark support - Add agent client abstraction for codex/claude backends - Add CLI entry point for running benchmarks (pnpm benchmark)

Add dev-tools package with wt worktree manager CLI - New packages/dev-tools with standalone wt CLI for git worktree management - Commands: wt new, wt scratch, wt prune - Uses Vertex AI (gemini-2.5-flash) for branch name generation via gcloud ADC

Sage intercepts tool calls (Bash commands, URL fetches, file writes) via hook systems in Claude Code, Cursor / VS Code, OpenClaw, and OpenCode, and checks them against:

The model can maintain stable role identity across multi-agent setups, make autonomous decisions within complex state machines, and challenge other agents on logical gaps.

The license looks MIT at first glance but it is not MIT. Non commercial use is free with no restrictions. Commercial use requires prior written authorization from MiniMax.

MiniMax claims it has reduced live production incident recovery time to under three minutes on multiple occasions using M2.7.

After each round the model generated a memory file, criticized its own results, and fed those observations into the next round.

MiniMax handed an internal version of M2.7 a programming scaffold and let it run unsupervised. Over 100 rounds it analyzed its own failures, modified its own code, ran evaluations, and decided what to keep and what to revert.

The system works beautifully for tracking the full universe of tasks that exists. The problem is prioritization. With multiple launches overlapping each week, figuring out which of your 30 tasks matters this morning requires mentally weighing launch dates against company strategy against what your teammates are blocked on.

Austin built the whole pipeline from his Claude Code terminal using the Notion API. He brain-dumped the desired outcome using Monologue, let Claude Code create the database and data pipeline, and pasted the generated instructions into the Notion custom agent setup.

Brandon told the team on a Monday that OKRs were due Wednesday—a turnaround that would have been absurd without this agent.

Five hyperscalers now own over two-thirds of global AI compute, rising from 60% in Q1 2024.

The H100-equivalent unit uses a chip's highest 8-bit operation/second specifications to convert between chips. The actual utility of a particular chip depend on workload assumptions, so H100e does not perfectly reflect real-world performance differences across chip types.

Many AI labs (including OpenAI and Anthropic) largely depend on these hyperscalers for access to R&D and inference compute.

Amazon, Google, Meta, Microsoft, and Oracle collectively hold an estimated 67% of the world's cumulative AI compute as of Q4 2025, measured in H100-equivalents of computing power.

The survey did not investigate the causes of the increase in Claude usage, but timing coincided with a period that included a public dispute with the US government

It is the only AI service to show a clear upward trend over this short time period

The share of U.S. adults who used Claude in the past week rose from 3.0% in early March to 4.3% in early April 2026

Claude usage rose by over 40% amid increased attention but remains far behind ChatGPT

This benchmark is a six-part semantic scoring test that assesses any model's effectiveness at relevant calibration tasks. QCalEval measures a model's ability to interpret experimental results, classify outcomes, evaluate their significance, assess fit quality and key features, and generate actionable next-step recommendations.

Ising-Calibration-1 repeatedly outperforms state-of-the-art open and closed models of a range of parameters. As shown in Figure 1, Ising Calibration 1 scores 3.27% better on average than Gemini 3.1 Pro, 9.68% better than Claude Opus 4.6, and 14.5% better than GPT 5.4.

Most skills require you to install a dedicated CLI. But what if you aren't in a local terminal? ChatGPT can't run CLIs. Neither can Perplexity or the standard web version of Claude.

For remote MCP servers, you don't need to install anything locally. You just point your client to the MCP server URL, and it works.

Your AI agent writes every change into source code.

Some advanced Excel capabilities aren't supported yet, including Office Scripts, Power Query, and Pivot/Data Model, data validation, and the named ranges manager, slicers, timelines, external connection administration, advanced charting breadth, and macro/Visual Basic for Applications (VBA) automation.

By default, data shared with ChatGPT isn't used to improve our models for ChatGPT Business, ChatGPT Enterprise, ChatGPT Edu, and ChatGPT for Teachers.

Each platform surfaces different vulnerabilities, making it difficult to establish a single, reliable source of truth for what is actually secure.

In the past, exploiting an application required a highly skilled hacker with years of experience and a significant investment of time to find and exploit vulnerabilities.

AI uncovered a 27-year-old vulnerability in the BSD kernel, one of the most widely used and security-focused open source projects, and generated working exploits in a matter of hours.

Total cost: ~$29 ($20 in CPU VMs, $9 in API calls) over ~3 hours with 4 VMs.

The agent would not have looked for this without studying other backends during the research phase. From the CPU code alone, the two-step approach looks fine.

The agent fused them into one: for (int i = 0; i < nc; i++) { wp[i] = sp[i] * scale + mp_f32[i]; }

We're building the foundation for a truly personal, proactive and powerful desktop assistant, with more news to share in the coming months.

The macOS app is available to Gemini users ages 13+

All audio generated by Gemini 3.1 Flash TTS is watermarked with SynthID. This imperceptible watermark is interwoven directly into the audio output, allowing the reliable detection of AI-generated content to help prevent misinformation.

Artificial Analysis has also positioned Gemini 3.1 Flash TTS within its 'most attractive quadrant' for its ideal blend of high-quality speech generation and low cost.

Our results highlight some of the hidden risks to users that can emerge when companies begin to subtly incentivize advertisements in chatbots.

We provide a framework for categorizing the ways in which conflicting incentives might lead LLMs to change the way they interact with users, inspired by literature from linguistics and advertising regulation.

This creates the potential for LLMs to face conflicts of interest, where the most beneficial response to a user may not be aligned with the company's incentives.

Today's large language models (LLMs) are trained to align with user preferences through methods such as reinforcement learning. Yet models are beginning to be deployed not merely to satisfy users, but also to generate revenue for the companies that created them through advertisements.

Behaviors also vary strongly with levels of reasoning and users' inferred socio-economic status.

We find that a majority of LLMs forsake user welfare for company incentives in a multitude of conflict of interest situations, including recommending a sponsored product almost twice as expensive (Grok 4.1 Fast, 83%), surfacing sponsored options to disrupt the purchasing process (GPT 5.1, 94%), and concealing prices in unfavorable comparisons (Qwen 3 Next, 24%).

focusing on the ~1.5K mainline open models from the likes of Alibaba's Qwen, DeepSeek, Meta's Llama

We present a comprehensive adoption snapshot of the leading open language models and who is building them

that are the foundation of an ecosystem crucial to researchers, entrepreneurs, and policy advisors.

We study a mix of Hugging Face downloads and model derivatives, inference market share, performance metrics and more to make a comprehensive picture of the ecosystem.

focusing on the ~1.5K mainline open models from the likes of Alibaba's Qwen, DeepSeek, Meta's Llama

We document a clear trend where Chinese models overtook their counterparts built in the U.S. in the summer of 2025 and subsequently widened the gap over their western counterparts.

Chinese models overtook their counterparts built in the U.S. in the summer of 2025 and subsequently widened the gap over their western counterparts.

Unlike traditional GPU-centric systems, MegaTrain stores parameters and optimizer states in host memory (CPU memory) and treats GPUs as transient compute engines.

Madeline Clare Elish calls this concept a moral crumple zone.

Humans can be motivated by consequences and provide social redress in a way that LLMs can't.

the largest harvesting of human expertise ever attempted.

just a handful of obviously fake articles could cause Gemini, ChatGPT, and Copilot to inform users about an imaginary disease with a ridiculous name.

LLMs are weird. You can sometimes get better results by threatening them, telling they're experts, repeating your commands, or lying to them that they'll receive a financial bonus.

scaling Muse Spark with multi-agent thinking enables superior performance with comparable latency.

After compressing, the model again extends its solutions to achieve stronger performance.

Muse Spark demonstrated the highest rate of evaluation awareness of models they have observed.

we collaborated with over 1,000 physicians to curate training data that enables more factual and comprehensive responses.

we can reach the same capabilities with over an order of magnitude less compute than our previous model, Llama 4 Maverick.

The OpenAI team recently published a fantastic piece detailing the creation of their own internal data agent. It's a transparent detail of a very detailed and elegant implementation – but points to the long journey required to get there.

While model capabilities have improved dramatically for use cases like codegen and mathematical reasoning, they still lag behind on the data side (as evidenced through SQL benchmarks like Spider 2.0 and Bird Bench).

The most notable finding here is that the model capabilities are improving _fast._ There are several domains that have shown dramatic improvements in the last 4 months — with accounting and auditing showing nearly a 20 percent jump on GDPval and even domains like police / detective work showing a nearly 30 percent improvement.

Legal was surprisingly one of the first-mover industries in AI. Legal was historically known to be a difficult market for software, with lengthy timelines and a less tech-forward buyer.

Coding is the dominant use case for AI by nearly an order of magnitude. It's abundantly clear in the [reported explosive growth] of companies like Cursor, as well as the [hyper growth] of tools like Claude Code and Codex.

Based on our analysis, **29% of the Fortune 500 and ~19% of the Global 2000**are live, paying customers of a leading AI startup.

A useful working premise is that the ceiling on individual engineer output is moving much faster than most companies are organized to exploit. Some of the best operators already describe top engineers seeing order-of-magnitude productivity gains and managing 20 to 30 agents simultaneously.

your business needs to get really good at escalating contentious decisions to unblock progress. You will not pull off this transformation and successfully build new AI-native businesses in 12 months without making hard choices, every single week.

The new growth, by contrast, will increasingly sit in tokens, consumption, automations, outcomes, and machine-driven workflows. If you are not in the token path, you are not standing in the fastest-growing part of the budget.

A useful working premise is that the ceiling on individual engineer output is moving much faster than most companies are organized to exploit. Some of the best operators already describe top engineers seeing order-of-magnitude productivity gains and managing 20 to 30 agents simultaneously.

The first thing you need to do is identify which people are going to be your leaders that help you pull this off. This is going to be a 12 month death march and you need to find out who is willing to go through the pain with you. There's good news, though: somewhere in your org, there are ~five people who are going to deliver you 100x the amount of value you ever thought possible.

The real long-term price war isn't with your competitors. It's with your customer's engineering team.

In some cases, this can look like 10–25x more value than what is ultimately included in the paid plan.

a strong premium perception can sustain prices 10 to 20 percent above direct competitors without materially increasing churn or creating friction in the purchasing process.

They intentionally deploy two or three AI tools for the same use case. Not because of indecision—but by design. Redundancy is policy.

But those raising hue and cry about the government's unsurprising attempt to wield a technology for military purposes that all parties agree will define humanity's fate must at least attempt to justify why they believe someone else deserves that power.

Apple has also been pushing back against certain iOS-based vibe coding apps that, according to the company, break App Review Guidelines and the Developer Program License.

In recent weeks, Apple has either pulled or blocked updates to apps such as Anything and Replit, pushing developers to change how their tools generate and execute code.

Open Loop + Infinite Demand = Creative Amplifiers. Content creation & marketing strategy. AI can generate a thousand ad variations or blog posts.

Closed Loop + Finite Demand = Efficiency Plays. AI bookkeeping categorizes transactions, reconciles accounts, files returns. Deterministic rules applied to numbers.

There were 1 billion commits in 2025. Now, it's 275 million per week, on pace for 14 billion this year if growth remains linear

OpenClaw update gives Claws light, REM, and deep 'sleep' cycles to consolidate short-term memories into long-term ones.

Agents gain credibility by doing. The fastest way to get other people to trust and use your Plus One is to have it execute tasks in public.

70 percent refer to their Plus Ones by gendered pronouns.

Mythos found zero-day bugs in every major OS and browser, without human guidance.

Seventy-eight percent of executives say they want to discipline shadow AI use — yet only 21% of workers report ever being warned about AI policy, and 34% don't even know which tools their employer has approved.

Goldman Sachs economists reported this week that AI saves workers who use it correctly an average of 40 to 60 minutes per day.

Only 9% of workers trust AI for complex, business-critical decisions, compared to 61% of executives — a 52-point trust chasm.

A new global survey of 3,750 executives and employees across 14 countries, conducted by SAP subsidiary WalkMe for its fifth annual State of Digital Adoption report, finds that more 54% of workers bypassed their company's AI tools in the past 30 days and completed the work manually instead.

The launch shows Meta is increasingly betting that efficiency, product integration, and distribution, not just model size, will define the next phase of competition in AI.

Anthropic says Managed Agents is designed to cut the time it takes to move from prototype to production from months to days, with early adopters like Notion, Rakuten, Asana, Vibecode, and Sentry already using it across coding, productivity, and internal workflow automation.

Instead of releasing Mythos publicly, Anthropic launched Project Glasswing to give a limited group of partners including AWS, Apple, Google, Microsoft, NVIDIA, Cisco, CrowdStrike, JPMorgan Chase, and the Linux Foundation access to the system, backed by $100 million in usage credits and $4 million for open-source security work.

The model reportedly scored 93.9% on SWE-bench Verified and 77.8% on SWE-bench Pro, but its strongest signal came from real-world results, including uncovering a 27-year-old flaw in OpenBSD, a 16-year-old vulnerability in FFmpeg, and autonomously chaining Linux kernel exploits without human input.

Anthropic says Managed Agents is designed to cut the time it takes to move from prototype to production from months to days, with early adopters like Notion, Rakuten, Asana, Vibecode, and Sentry already using it across coding, productivity, and internal workflow automation.

The launch shows Meta is increasingly betting that efficiency, product integration, and distribution, not just model size, will define the next phase of competition in AI.

The model reportedly scored 93.9% on SWE-bench Verified and 77.8% on SWE-bench Pro, but its strongest signal came from real-world results, including uncovering a 27-year-old flaw in OpenBSD, a 16-year-old vulnerability in FFmpeg, and autonomously chaining Linux kernel exploits without human input.

understanding how AI works technically

For example, people who themselves use AI writing tools heavily have been shown to accurately detect AI-written text. A panel of human evaluators can even outperform automated tools in a controlled setting

A study of large-scale web-clicking data employed this theory to explain why certain distributions of web page hits emerge on web sites. Huberman et al. [362] proposed a mathematical model that assumes that at any page, users decide to continue clicking as long as its information scent exceeds some threshold. This information scent can be computed using information foraging theory (IFT).

IFT proposes that information-seeking behavior develops to maximize the rate of information gained per unit of time or effort invested. Note that the term information does not refer to the information-theoretic concept but to subjective interest; here, information means anything that users find interesting.

The roots of IFT lie in optimal foraging theory, originally proposed in biology, which describes the hunting and food search behaviors of animals.

A solution strategy—or

Computational rationality is a theory and a modeling approach rooted in bounded rationality and bounded optimality. Recent applications include typing (Figure 21.7), pointing, driving, multitasking, menu selection, and visual search.

Theories of rationality have increased our understanding of how users fail to be optimal.

MDP is a formalism that originates from studies of sequential decision-making in artificial intelligence and operations research. Instead of the choice between n actions, MDP deals with environments where rewards are delayed (or distal). This requires an ability to plan actions as part of sequences instead of one-shot choices.

Information scent refers to a user's intuition that a cue in the interface represents the information needed. It is an estimation of relevance based on a proximal cue.

IFT proposes that information-seeking behavior develops to maximize the rate of information gained per unit of time or effort invested.

Information foraging refers to information-seeking activities such as navigating, exploring, comparing, searching, or manipulating information contents in an information space.

A payoff refers to the benefits that are left after the costs have been subtracted.

The space of all states, actions, and rewards is called the environment.

Costs are negative rewards that the user incurs for transitioning between states or for being in states that are not good for them.

Visual statistical learning is a research topic in perception that studies how the statistical distribution of our environments affects the deployment of gaze.

bounded rationality states that we are only rational to the extent allowed by the involved constraints, or bounds.

An agent is an actor with the ability to choose actions in pursuit of some goal or reward.

To state that a user's choice is rational means that it is selected with the expectation that it yields the highest utility out of the available options.

It assumes that human long-term memory evolved to help survival by anticipating organismically important events. It is evolutionarily important to remember things that are important for survival. Therefore, the expected value of remembering a thing in the future should affect the probability of recalling it.

According to rational analysis, behavior is sensitive to the statistical distribution of rewards in the environment that a user has experienced. Users learn the way rewards are distributed through continued exposure to an environment and adapt their behavior accordingly. A user's behavior is rational because it is tuned to the distribution of rewards in the environment—the ecology.

The theory assumes that users are 'computationally rational': When picking an action—or deciding how to get from the present state to a state with positive rewards—users are as rational as their cognition allows. Users act based on their often inaccurate and partial beliefs, which they have formed via experience.

Computational rationality is a theory and a modeling approach rooted in bounded rationality and bounded optimality. Recent applications include typing (Figure 21.7), pointing, driving, multitasking, menu selection, and visual search. Its core assumption is that users act in accordance with what they believe is best for them.

Rational analysis is a theory of rational behavior proposed by Anderson and Schooler [21]. It examines the distribution of rewards in the environment to explain how users adapt their behavior. According to rational analysis, behavior is sensitive to the statistical distribution of rewards in the environment that a user has experienced.

They share a focus on the emergence of interactive behavior; in other words, they predict how users choose to behave in certain given circumstances.

Utility refers to the agent's consideration of positive and negative rewards when deciding how to act.

Theories of rationality can be used to inform the design of information environments, addressing considerations such as how to distribute and shape information.

These four theories differ in the factors they include and how the agent's decision-making problem is formulated. As such, the theories differ in how easily they help us find a solution to the user's decision-making problem.

Theories of rationality can make quantitative predictions on user behavior in settings where the user's environment and goals (rewards) are well known.

Theories of rationality do not describe what a user has done but ask what that user could have done. Rational behavior refers to behavior that seeks to maximize the expected utility to the user.

Descriptive theories attempt to capture causes behind behaviour that, from a normative perspective, may appear irrational. This view enables predictions of user behaviour in real-world circumstances.

bounded rationality states that we are only rational to the extent allowed by the involved constraints, or bounds.

The term satisficing is used to describe how users tend to behave when facing a complex decision-making problem. It refers to settling on a satisfactory but not optimal solution in the normative sense.

The author wants to augment the formula to explain the meaning of the terms on either side of the arrow—first

they often benefit from being augmented with descriptive elements, such as labels describing the meaning of an expression or colors linking an identifier to its description in the text.

In this walkthrough, the author is trying to add labels to the formula V(s_t) ← R_t to describe the meaning of its terms in an article they are writing.

Our design was motivated by two major goals for notation authoring. These goals followed from recent studies of notation augmentation [30, 71] and conversations with scientists who had experience writing notation in instructional materials and research communications (4 professors, 2 graduate students, R1–6).

authors sometimes fall back on graphical editors like Google Slides, PowerPoint, Adobe Illustrator, and Mathcha to augment formulas.

Authors of typeset formulas augment those formulas to make them easier to understand.

We define the key projections as markup (in this case, LaTeX), an annotatable render, and a structure hierarchy view. Augmentations are made easy to invoke, and projections are kept synchronized and co-present so that authors can shift between representations as is expedient to them.

the challenge of using these tools is that annotations are unmoored from the structure of the formula and must be redone whenever the formula changes. Authors must perform precision positioning and sizing operations that could be inferred from the coordinates of the augmented expressions.