Salesforce

Major 2026 Tech Layoffs Citing AI

TechCrunch compiles a running list of major 2026 tech company layoffs where executives cited AI as a factor. The roundup notes roughly 120,000 tech roles cut in 2026 (according to Layoffs.fyi) and details large reductions at multiple large employers: Microsoft eliminated about 4,800 roles, Oracle disclosed a 21,000-headcount reduction over 12 months tied in part to AI, Meta cut ~8,000 roles while moving ~7,000 into AI-focused jobs, and Amazon cut 16,000 corporate positions. Other companies including GitLab, Intuit, Cisco, Cloudflare, Coinbase, PayPal, Snap, IBM, Atlassian, Dell, Block and Salesforce are listed with layoffs or restructurings explicitly linked to AI adoption, infrastructure shifts or organizational simplification. The piece highlights a broader industry pattern of rising revenues alongside workforce reductions attributed to AI-driven efficiency and role rebalancing.

Build a Hermes-style Agent Workflow

The article explains a Hermes Agent–style architecture for chaining AI tasks while controlling token costs and preserving institutional context. Instead of sending raw data to an external model for every request, the pattern stores data on infrastructure you control (context store), keeps reusable guidance and voice rules in a searchable skill library, and uses a minimal-prompt extractor to pass only a tiny, relevant slice to an LLM. The approach is provider-agnostic (you can swap models) and reduces per-call token bills while accumulating reusable knowledge that improves over time. The piece also warns teams to evaluate model choice, handle procurement/security for API access, and notes providers such as Anthropic enforce API terms against subscription-routing workarounds.

Vision-Language Models: How AI Sees and Talks

This technical explainer (Part 3 of a 3-part series) surveys the rise of vision-language models (VLMs), describing the evolution from early image-captioning systems through contrastive pretraining (CLIP) to generative multimodal models and natively multimodal transformers. It outlines four architectural approaches (contrastive dual-encoders, cross-attention fusion, projection of visual tokens into LLMs, and natively multimodal unified transformers), lists major models and vendors (e.g., CLIP/OpenAI, Flamingo/DeepMind, BLIP/Salesforce, LLaVA, GPT-4V/OpenAI, Gemini/Google, Claude/Anthropic, Qwen-VL/Alibaba, InternVL), and surveys real-world applications (VQA, OCR/document understanding, robotics, medical imaging, creative workflows). The article closes with key challenges—hallucination, spatial and temporal reasoning, fine-grained perception, safety/bias—and trends toward unified multimodal training, chain-of-thought visual reasoning, smaller specialized models, and real-time multimodal agents.