TLDR AI

TLDR AI 2026-06-23

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01GENERAL

Mercury Command is that for your finances.

Say what you need and the work gets done — payments, forecasting, categorization, invoices — across all of Mercury. You approve every action, and every answer is generated from your Mercury data with full account context and a traceable record.

mercury.com

TRY MERCURY COMMAND →

_*Mercury is a fintech company, not an FDIC-insured bank. Banking services provided through Choice Financial Group and Column N.A., Members FDIC._

mercury.com

02HEADLINES & LAUNCHES

SPACEX SIGNS COMPUTING POWER DEAL WITH OPEN-SOURCE AI STARTUP REFLECTION WORTH UP TO $6.3 BILLION

SpaceX signed a deal worth up to $6.3 billion with Reflection AI for access to its supercomputer Project Colossus. This agreement allows Reflection to use Nvidia GB300s for training open-source AI models, reflecting a shift toward increased demand for compute power in AI development. SpaceX aims to capitalize on this by offering its infrastructure to outside AI companies, bolstering its position in the AI and data center space.

cnbc.com4 min read

OPENAI LAUNCHES NEW SECURITY TOOLS AND UPDATES GPT-5.5-CYBER

OpenAI recently launched an updated Codex Security plugin, GPT-5.5-Cyber (in limited release), a Daybreak Cyber Partner Program, and an open source security initiative called Patch the Planet. Daybreak is a defensive cyber stack for the AI era. OpenAI is promoting Daybreak through a partner model rather than direct broad model access. It aims to embed GPT-5.5 with Trusted Access for Cyber into existing security products and services while keeping access governed through partner systems.

testingcatalog.com2 min read

ALIBABA'S AI VIDEO MODEL RISES TO NO. 2 IN GLOBAL RANKINGS, AS OPENAI'S SORA AND BYTEDANCE'S SEEDANCE FALL AWAY

Alibaba's HappyHorse 1.1 AI video generation model delivers production-ready video through an API built for integration into enterprise software stacks. It is now live on Alibaba Cloud Model Studio with a 40% sitewide launch discount for the first two weeks. HappyHorse supports text-to-video, image-to-video, and subject-to-video generation, as well as video editing. Its abilities cover the full spectrum of commercial video needs, from ideation through production to post-production.

venturebeat.com14 min read

03DEEP DIVES & ANALYSIS

GLM-5.2 RAISES THE BAR FOR OPEN MODELS

TheZvi examined GLM-5.2's capabilities and benchmark results, arguing it represents a significant improvement over previous open models. The analysis positioned it as one of the strongest openly available models while still trailing the leading frontier systems.

thezvi.wordpress.com14 min read

THE TEXT IN CLAUDE CODE'S “EXTENDED THINKING” OUTPUT IS NOT AUTHENTIC

Claude Code's 'Extended Thinking' reasoning is encrypted. Anthropic holds the key, and users' machines never receive it. The API returns a summary of the reasoning, and not the reasoning itself. Receiving the full thinking output requires an enterprise agreement.

patrickmccanna.net3 min read

MODEL SIZE SCALING IN 2023-2031

Targeting a particular speed of token generation puts a constraint on the total parameters of the model. If there isn't enough pretraining compute, models will remain smaller. This article looks at these considerations and estimates model sizes feasible for each year between 2023 and 2031. There are many assumptions that go into the estimates, which predict total parameters for models to reach 1.4 quadrillion in 2031.

lesswrong.com21 min read

KNOWLEDGE AGENTS: BEAT FRONTIER MODELS WITH BETTER STRUCTURE

Anthropic pulled its Mythos model, while the article's author developed smaller agentic models called "knowledge agents" to match larger frontier models. These agents enhance AI by injecting specific, relevant knowledge, performing well even with smaller models like Qwen 3.6 27B. The methodology involves embedding, structuring data, and multiple search passes, successfully augmenting LLMs for specialized queries and proprietary data.

weightythoughts.com18 min read

04ENGINEERING & RESEARCH

Sponsored

LLMS AREN'T BUILT TO FIT YOUR USE CASE, BUT THIS ROUTER PICKS A MODEL THAT DOES

LLMs have billions of parameters, built to do everything, which means they're optimized for nothing you actually need. Pioneer's model router for coding tasks monitors your inference requests, analyzes task complexity, and it automatically surfaces leaner model options for the job for faster, cheaper, more accurate workflows.

pioneer.aiSponsor

MOEBIUS

Moebius is a highly efficient lightweight inpainting framework. The 0.22B model rivals and even surpasses the generation quality of the 11.9B industrial generalist FLUX.1-Fill-Dev. It does this with an under 15x acceleration in total inference time. Moebius liberates real-world image inpainting and AI object removal from parameter bloat.

hustvl.github.io4 min read

USING CODEX FOR LONG-RUNNING PROJECTS

This guide outlines strategies for treating Codex as a persistent workspace that can maintain context across extended projects. It covers workflow management, task decomposition, and techniques for balancing autonomous execution with human oversight.

links.tldrnewsletter.com18 min read

05MISCELLANEOUS

BONUS, FULLY REMOTE)

We're hiring a senior PMM to own product marketing at TLDR. You'll define our positioning, build out sales enablement, and lead every launch. Learn more [18].

jobs.ashbyhq.com

ANTHROPIC SAYS CLAUDE MAY WANT TO SEE YOUR ID

Anthropic may require identity verification in certain circumstances starting on July 8. The company has not provided specific examples of when users will be asked to provide government-issued documents. An Anthropic spokesperson claims that the change will only apply to a small subset of users whose accounts are flagged but not outright banned. Anthropic uses Persona as its identity-checking provider.

techcrunch.com4 min read

LOOP ENGINEERING CLEARLY EXPLAINED

Loop engineering shifts AI development from manually prompting agents to designing autonomous systems that decide what to work on, execute tasks, verify outcomes, and improve over time. The core agent loop is simple, but the hard problems are defining reliable stopping conditions, preventing context rot, designing agent-friendly tools, and building verification mechanisms that can independently judge success.

links.tldrnewsletter.com7 min read