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BD Tech Talks

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01
How Nvidia’s ASPIRE framework accelerates robot programming with self-improving AI
ASPIRE and the new era of self-improving AI frameworks are drastically reducing token costs and deployment friction for real-world robotics applications. The post How Nvidia’s ASPIRE framework accelerates robot programming with self-improving AI first appeared on TechTalks .Ben Dickson
02
How the AI arms race moved from smart models to full-stack infrastructure
A breakdown of how OpenAI, Nvidia, Google, and Amazon are shifting their development strategies to capture value across every layer of the tech stack. The post How the AI arms race moved from smart models to full-stack infrastructure first appeared on TechTalks .Ben Dickson
03
Demystifying loop engineering: Get more from AI agents, avoid loopmaxxing
The complete guide to the new loop engineering trend. Write powerful agentic loops while avoiding loopmaxxing. The post Demystifying loop engineering: Get more from AI agents, avoid loopmaxxing first appeared on TechTalks .Ben Dickson
04
Why LLMs should stop thinking out loud (and what comes after chain-of-thought)
Chain-of-Thought prompting is slow, expensive, and largely an illusion. The future of machine reasoning happens in latent space. The post Why LLMs should stop thinking out loud (and what comes after chain-of-thought) first appeared on TechTalks .Ben Dickson
05
Beyond vibe coding: How Codev 3.0 engineers the AI-powered dev team
Casual AI prompting breaks down as codebases grow. Codev introduces strict protocols and multi-model reviews to help teams ship maintainable software. The post Beyond vibe coding: How Codev 3.0 engineers the AI-powered dev team first appeared on TechTalks .Ben Dickson
06
Why the future of agentic AI is all about the harness
Scaling LLMs hits limits when dealing with agentic AI tasks. For that, we need to look at the harness and the system built around the model(s). The post Why the future of agentic AI is all about the harness first appeared on TechTalks .Ben Dickson
07
How Cursor’s Composer 2.5 uses self-distillation to beat the frontier LLMs at coding
A deep look at the self-distillation techniques that make Composer 2.5 such a great coding model (and the hidden tradeoffs they introduce to AI reasoning). The post How Cursor’s Composer 2.5 uses self-distillation to beat the frontier LLMs at coding first appeared on TechTalks .Ben Dickson
08
Vertical integration as AI infrastructure: What 21D’s full arch implant system teaches us about building autonomous clinical AI
A technical breakdown of how 21D built an end-to-end autonomous AI pipeline for one of medicine's most complex procedures — and the architectural decisions that made it work The post Vertical integration as AI infrastructure: What 21D’s full arch implant system teaches us about building autonomous clinical AI first appeared on TechTalks .Ben Dickson
09
Why sandboxing OpenClaw doesn’t stop data exfiltration
Research into Nvidia’s NemoClaw reveals that sandboxes don't stop AI agents like OpenClaw from leaking data. We need to rethink security from first principles. The post Why sandboxing OpenClaw doesn’t stop data exfiltration first appeared on TechTalks .Ben Dickson
10
Google brings multi-token prediction Gemma 4 LLMs
How Gemma 4’s multi-token prediction and community-driven DFlash are speeding up local LLM throughput by 3-6x. The post Google brings multi-token prediction Gemma 4 LLMs first appeared on TechTalks .Ben Dickson
11
How Memory Sparse Attention scales LLM memory to 100 million tokens
Memory Sparse Attention (MSA) scales LLM context windows to an unprecedented 100 million tokens while preserving accuracy. The post How Memory Sparse Attention scales LLM memory to 100 million tokens first appeared on TechTalks .Ben Dickson
12
Claude Code is leaking API keys into public package registries
A new study reveals how AI coding assistants like Claude Code are quietly hoarding and publishing sensitive API keys to code repositories. The post Claude Code is leaking API keys into public package registries first appeared on TechTalks .Ben Dickson
13
Anthropic’s MCP vulnerability: When ‘expected behavior’ becomes a supply chain nightmare
Security researchers have uncovered a massive architectural flaw in Anthropic's Model Context Protocol, exposing millions of AI applications to remote takeovers. The post Anthropic’s MCP vulnerability: When ‘expected behavior’ becomes a supply chain nightmare first appeared on TechTalks .Ben Dickson
14
The paradox of LLM self-distillation: Faster reasoning, weaker generalization
Optimizing LLMs for concise answers can destroy their ability to explore alternative solutions on difficult problems. New study reveals the hidden cost of self-distillation. The post The paradox of LLM self-distillation: Faster reasoning, weaker generalization first appeared on TechTalks .Ben Dickson
15
Why harness engineering is becoming the new AI moat
The recent leak of Anthropic's Claude Code reveals a hard truth: as LLMs become commoditized, the sophisticated engineering harness built around them is becoming the real moat. The post Why harness engineering is becoming the new AI moat first appeared on TechTalks .Ben Dickson
16
TopDawg vs Zendrop for US Dropshipping – Which Platform Is Better in 2026?
By Raphael Korobka In short: For merchants focused exclusively on selling to US customers, TopDawg is usually the stronger pick. Its supplier network is built around US-based fulfillment, and its hands-on support model suits retailers who want a partner that can provide crucial help. Zendrop works well for sellers who need global reach and a […] The post TopDawg vs Zendrop for US Dropshipping – Which Platform Is Better in 2026? first appeared on TechTalks .Contributor
17
How GhostClaw malware targets the OpenClaw AI agent boom
As developers rush to run local AI agents on Mac Minis, GhostClaw malware exploits macOS binaries to silently harvest credentials. The post How GhostClaw malware targets the OpenClaw AI agent boom first appeared on TechTalks .Ben Dickson
18
Why Meta’s V-JEPA 2.1 model is a massive step forward for real-world AI
AI models have historically struggled to balance motion tracking with spatial detail. Meta’s V-JEPA 2.1 solves this, pushing the boundaries of video self-supervised learning. The post Why Meta’s V-JEPA 2.1 model is a massive step forward for real-world AI first appeared on TechTalks .Ben Dickson
19
Multi-level AI prompt engineering: A new tool for scientific discovery
How multi-level prompt engineering and parabolic extrapolation transformed an LLM into a theoretical collaborator, yielding a testable model of the multiverse. The post Multi-level AI prompt engineering: A new tool for scientific discovery first appeared on TechTalks .Alex Kostikov
20
Why AI won’t kill SaaS
The recent tech selloff sparked fears of a SaaSpocalypse. Here is why the death of software subscriptions is a myth, and how AI agents are creating a developer boom. The post Why AI won’t kill SaaS first appeared on TechTalks .Ben Dickson
21
How C-JEPA is teaching AI the physics of the physical world
By forcing AI to understand cause and effect instead of just predicting pixels, C-JEPA is laying the groundwork for smarter, more predictable autonomous systems. The post How C-JEPA is teaching AI the physics of the physical world first appeared on TechTalks .Ben Dickson
22
How Databricks’ FlashOptim cuts LLM training memory by 50 percent
Training large language models usually requires a cluster of GPUs. FlashOptim changes the math, enabling full-parameter training on fewer accelerators. The post How Databricks’ FlashOptim cuts LLM training memory by 50 percent first appeared on TechTalks .Ben Dickson
23
How sparse attention solves the memory bottleneck in long-context LLMs
As AI agents take on longer tasks, the KV cache of LLMs has become a massive bottleneck. Discover how sparse attention techniques are freeing up GPU memory. The post How sparse attention solves the memory bottleneck in long-context LLMs first appeared on TechTalks .Ben Dickson
24
How ‘semantic chaining’ jailbreaks image generation models
Semantic Chaining exploits the fragmented safety architecture of multimodal models, bypassing filters by hiding prohibited intent within a sequence of benign edits. The post How ‘semantic chaining’ jailbreaks image generation models first appeared on TechTalks .Ben Dickson
25
How Sakana AI’s new technique solves the problems of long-context LLM tasks
RePo, Sakana AI’s new technique, solves the "needle in a haystack" problem by allowing LLMs to organize their own memory. The post How Sakana AI’s new technique solves the problems of long-context LLM tasks first appeared on TechTalks .Ben Dickson
26
Smarter trade: How AI turns regulatory burden into competitive edge
Stop reacting to compliance violations and start preventing them. See how AI empowers organizations to turn regulatory discipline into an engine for innovation and growth. The post Smarter trade: How AI turns regulatory burden into competitive edge first appeared on TechTalks .Contributor
27
Recursive Language Models: A new framework for infinite context in LLMs
Brute-forcing larger context windows is hitting a mathematical wall. Here is how MIT’s new framework solves "context rot" to process 10 million tokens and beyond. The post Recursive Language Models: A new framework for infinite context in LLMs first appeared on TechTalks .Ben Dickson
28
Microsoft’s new Rho-alpha model brings tactile sensing to robotics
Microsoft’s Rho-Alpha upgrades Vision-Language-Action models with tactile data to bridge the gap between semantic reasoning and low-level motor control. The post Microsoft’s new Rho-alpha model brings tactile sensing to robotics first appeared on TechTalks .Ben Dickson
29
Vulnerability in Perplexity’s BrowseSafe shows why single models can’t stop prompt injection
Lasso Security compromised Perplexity’s BrowseSafe guardrail model for AI browsers, proving that "out-of-the-box" tools fail to stop prompt injection attacks. The post Vulnerability in Perplexity’s BrowseSafe shows why single models can’t stop prompt injection first appeared on TechTalks .Ben Dickson
30
How test-time training allows models to ‘learn’ long documents instead of just caching them
By treating language modeling as a continual learning problem, the TTT-E2E architecture achieves the accuracy of full-attention Transformers on 128k context tasks while matching the speed of linear models. The post How test-time training allows models to ‘learn’ long documents instead of just caching them first appeared on TechTalks .Ben Dickson

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