B2B deals are more likely to close when the purchase feels safe and defensible, not just superior. About 40% of deals stall because buying groups cannot agree, and 81% go to vendors already familiar to most stakeholders. Finance, legal, and procurement teams control roughly 50% of decision influence, making cross-functional trust critical. Buyers are 3X more likely to choose a heavily recommended vendor than one with a better product or lower price.
Daniel Bustamante explains how a dormant 50,000-person email list generated $150K in a 7-day launch using a simple three-email weekly system. The cadence includes a Monday curation email, a Wednesday deep dive, and a Friday story or case study to build trust. He covers how email and social work together, structuring lead magnets, and balancing nurture with sales in welcome flows. He also shows how AI tools like Claude help streamline launch campaigns.
A creator built a local LinkedIn content system in a weekend that replaces multiple SaaS tools and cuts daily workflow from 3 hours to about 20 minutes. The tool uses engagement data to surface trending topics, track content pillars, suggest reposts, and identify audience signals for outreach. It also generates adjacent content angles from past high performers and ensures balanced coverage across themes. By replacing feed scrolling with signal-based inputs, it streamlines ideation and distribution.
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This free course explains how AI search engines decide what to cite and recommend. It also covers competitor gap analysis and prompt research for AI discovery. It shows how to structure content for citation and build brand mentions across the web. The course has 12 lessons and takes about an hour and 26 minutes to complete.
A GitHub repository acts as a living brain, using markdown files to store priorities and decision logs. A background file holds key context while a journal file forces regular prioritization. A Claude Code routine runs in the cloud each night, scanning Slack, email, calendar, and meeting transcripts. It then drafts replies, updates tasks, surfaces summaries, and writes changes back to GitHub. Notion is used for drafting and editing, Slack is used for notifications through a custom bot, and the whole setup runs continuously without manual prompts.
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 [17].
Feedback loops rely on real critique and experimentation. These systems break when feedback is unstructured or detached from decisions. AI increases noise by producing abundant low-value feedback at scale. Teams also move away from direct cross-functional conversations. As speed becomes the priority, learning slows and signals get suppressed. Without strong feedback systems, companies drift and corrections become slower and more expensive.
TikTok's 2026 NewFronts framed the platform as a sequential storytelling environment that mirrors television with new ad formats. Prime Time lets brands serve up to three ads within a 15-minute high-engagement window, while TopReach bundles the first app-open and in-feed placement to capture attention during cultural moments. With only 26% of marketers using TikTok campaigns, the platform argues for greater budget reallocation from linear and streaming TV.
Negativity bias makes difficult customer experiences more memorable than positive ones. Unacknowledged friction therefore shapes long-term brand perception. Acknowledging complexity, even without fixing it, reduces resistance and builds psychological safety. Validation changes how friction is remembered by the customer. Embedding this across onboarding, digital flows, and automated communications turns acknowledgement into a journey-wide loyalty strategy rather than a support tactic.
FIFA restricts use of “World Cup”, “FIFA”, “FIFA World Cup 26”, logos, slogans, mascots, and the trophy in marketing, so use generic terms unless licensed.
Gen Z is securing board seats at companies, trading experience and mentorship for influence and insight into younger consumers that older boards often miss.
Jeff Bezos' new startup, Prometheus, intends to create new engineering tools to improve the design and manufacture of practically any device. It aims to improve the efficiency of companies that design and build computers, automobiles, spacecraft, and other physical products. The tools it will create will accelerate the invention loop. The work done at Prometheus could benefit Bezos' other companies.
SpaceX raised $75 billion in its IPO. The IPO, the biggest so far, drew demand more than four times the available shares. SpaceX's shares will debut on the Nasdaq and Nasdaq Texas on Friday. A successful showing in the public markets could tip the scales and make Elon Musk a trillionaire.
The Familiar is a soft, furry robot about the size of a dog created by Familiar Machines & Magic, a company started by Colin Angle, the creator of the Roomba. It uses AI to communicate and form intimate bonds with members of the household. The robot is designed to capture presence, not attention: it has no advertising model, engagement-driven feed, or incentive structure that rewards maximizing screen time or interaction time. While the Familiar can connect to the internet, by default, it will not send any data to the cloud and it will ask permission before accessing anything online.
NASA's Deep Space Network couldn't keep up with the routine demands of its science missions during the Artemis I mission nearly four years ago. The network reduced or delayed downlinks from several high-profile science missions. As a result, NASA put in new processes focused around coordination and scheduling. This produced better results during Artemis II, which had an even higher data requirement than the previous mission, but the limitations of the network and high demand continue to be an issue.
Learn how to govern non-deterministic architectures, consolidate tooling, and ship AI-native apps fast without hurting reliability. All in one morning. New Relic's NOW 2026 showcases exclusive data on AI generated code [9], how to wire observability into your AI development
Earlier models needed custom tools and everything spelled out to work. Today's models can absorb a ton of context and reason over raw data without choking. Bigger context windows tempt people to throw in more data, but noise causes accuracy to drop. Increase accuracy by building context like a memory hierarchy.
The most significant changes in Homebrew 6.0.0 are a new tap trust security mechanism, a new default internal Homebrew JSON API, sandboxing in Linux, better defaults, many 'brew bundle' improvements, improved performance, and initial support for macOS 27. Homebrew now requires taps to be explicitly trusted before code is evaluated or run, reducing the risk from malicious or compromised taps. All the details about what's changed since Homebrew 5.1.0 are available in the article.
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 [13].
Scott Alexander is a doctor on the US West Coast. He is currently working on new models for mental health care at Lorien Psychiatry. This post contains his thoughts on AI timelines, AI safety, geopolitics, and more.
Writing code was never the bottleneck in software development. Developers spend surprisingly little time coding, and studies have found that using agents to write code has led to little impact on overall productivity. The real bottlenecks are deciding and specifying what to build, verifying and being accountable for what is delivered, and the deep human understanding required to carry out both. AI doesn't make the 'decide' layer thinner - once a decision can be delegated to AI, it is no longer a source of competitive advantage - and humans still need to be accountable for what they deliver.
Terrence Tao didn't think AI would replace human mathematicians any time soon, but he thought the technology was well-suited to helping solve mathematical problems that could be broken into thousands of small, manageable subproblems.
Performance at tech companies is dominated by outlier work, so instead of grinding at 100% effort all the time, save some energy and time and use it to find these opportunities.
DeltaDB is a version control system built on a single coherent abstraction that transforms conversations with agents and the worktrees they edit into shared artifacts.
For use cases like B2C ecommerce, AI search is a non-negotiable part of a modern UX. Vibe coding makes the basic search experience easy to build - but difficult to maintain and validate.
OpenAI announced it would acquire Ona to bring secure cloud execution and orchestration capabilities into the Codex platform. The technology is intended to support persistent, customer-controlled environments where agents can continue working across extended periods and sessions.
Anthropic has decided to make its safeguards for frontier LLM development visible after backlash from researchers. The company had previously discreetly rerouted requests to a lesser model when asked to perform certain actions. Researchers found that Claude Fable 5 was either refusing or degrading responses for tasks like training competing models, debugging AI code, and optimizing neural architecture. This raised concerns about Anthropic's lack of transparency and also that tokens and money had been spent on a model that didn't do what was expected.
Frontier AI models are typically trained on sequences of integers known as tokens. Each token refers to some sequence of bytes, and these byte sequences often correspond to common words. This post presents an algorithm that can compute an optimal tokenizer in some settings.
CoreWeave's co-founder, Brannin McBee, recently claimed that compute isn't fungible the way a commodity has to be. He has a real argument, but the non-commodity framing is the keystone of his company's value. While he appears to be saying that there is no market, he's actually pricing the market and revealing where the spread still hides.
This post shares how a developer created their own LLM from scratch. It covers how they create their own base-training and fine-tuning scripts, data processing pipelines, and custom dataset. The total cost of the project was around $80, but they had a decent PC to process the data. The model and code are available in the post.
AI models are brilliant, but they're probabilistic. Without the ground truth of your operational reality, trusting AI is like trusting a fortune cookie. The Celonis Context Model [13] is a dynamic digital twin of your business that gives AI agents the operational clarity
MiMo Code V0.1.0 is an open source terminal-native AI coding assistant from Xiaomi. It outperforms Claude Code on key agentic coding benchmarks, particularly on long-horizon, multi-step tasks. MiMo Code features a cross-session memory system that uses an independent subagent to take notes of decisions, issues, and the scope of the project as it progresses. The model is available on GitHub under an MIT license.
This deep dive explores how PyTorch executes and optimizes neural network layers, progressing from individual linear operations to a fused multilayer perceptron.
Predictive data debugging identifies potential model behaviors before training by analyzing preference datasets. This technique, integrated into the Silico platform, allows engineers to reshape datasets or training processes to prevent undesired effects, improving both performance and safety. Case studies reveal common issues like compromised safety guardrails, hallucinated links, and context-specific sycophancy, allowing targeted interventions to fix these problems before deployment.
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].
Oracle has told investors to expect an additional $20 billion capital raise. It also reported negative free cash flow for the year. Oracle saw an increase in revenue for the fiscal fourth quarter, but its AI buildout has caused capital expenditures to jump 162% to $55.7 billion. Investors are concerned about whether the company's massive amount of spending will result in profit growth.
Model performance has only improved over time. There's currently no reason it shouldn't continue to improve in the future. Open weight models are only a few months behind frontier models on benchmarks. If current trends continue, it is likely that a Claude Fable 5-level open model that can run on a device with 16 GB of RAM will be possible by early 2029.