Demand for Chinese air conditioners has skyrocketed in many parts of Europe as heatwaves become more frequent, intense and prolonged this summer. PortaSplit, a portable split air conditioner from Chinese electronics manufacturer Midea, has become a hit product in the European market, with sales doubling from 2025. Midea's household air conditioner factory in Guangzhou has ramped up production to meet international market demand.

While extreme #heat# has exposed a critical infrastructure gap in Europe, #China#'s #Midea# has seen its no-drill portable air conditioners, Midea PortaSplit, sell out at many German retailers within days. It bypasses Germany's costly air conditioner installation barriers — fixed units can exceed 5,000 euros. The PortaSplit mounts on window frames without drilling, comes pre-charged and plug-and-play, and can be relocated or even powered by an EV for camping. It fits Germany's needs perfectly, tackling the high costs and installation hurdles that have hindered air conditioner adoption, bringing much-needed coolness to residents amid the scorching heat.

#Manufacture# | Recently, #Europe# has been hit by an extreme heatwave, triggering a rush among locals to buy air conditioners. According to local overseas Chinese, multiple models have already been sold out, including the PortaSplit air conditioner, which was specially designed for the European market by #Guangdong-based# Midea Group. Have a look: #Midea# #PortaSplit# #EuropeHeatwave# #MadeInGuangdong# #TechInnovation# #China#

Importing ≠ portability The big AI models will now let you export your chat history and import it somewhere else. But there’s two catches. 1. It only works between the big models. The smaller AI tools in your stack never see any of it. 2. It's a file dump, not a sync. Every time your context changes, you're updating it by hand. Context should be portable everywhere you work, instead of being held together by a million little workarounds.

🧸 Take your favorite Final Fantasy VII characters wherever you go with these Square Enix Store Shanghai plush keychains, combining adorable designs with everyday portability! ⬇️ Order today 🛒 #ffvii# #finalfantasyvii#

The moments that change everything demand technology that's ready for anything. Dell XPS meets the moment with premium craftsmanship, ultra-thin portability, and powerful performance. ​ Discover what's next. 👀 @DisclosureDay only in theaters 06.12.26

The next AI lock-in fight is simple: don't give away the data and context that make your company valuable @benchmark's @peterfenton says ceding that context to a closed model company "seems incoherent" and the real value is in workflows, proprietary data, how the business actually works If the big labs won’t guarantee ownership and portability, an independent ecosystem has to exist above them

Summary: I spent time trying to figure out this orchestration layer problem, can we design a multi model architecture in the long term. The more I dug in the more I understand that trying to build an abstracted layer is hard. As agentic activities increase and agent chaining and complex tasks get assigned to AI it will become harder to move between models. There is a reasonable probability that 75% of the enterprises will build their implementation of the solution to their core problem around one model "stack". Token price reduction by 90% is the solve and mobility between models from the same frontier lab! Evals, harnesses, cache memory are the moats and I don't see models providing simple abstraction to those. I know there are efforts to do this out there, the long term solve for orchestration if it works will need to be "Claude code" level of design genius. Here's a chat with Fable @HamzaFodderwala had. **Why abstraction looks easy.** Models are stateless — every API call is weights + a prompt assembled at runtime. Everything the model "knows" about you — memory, documents, history, tools — is injected into the context window by software outside the model. So in principle, all your state already lives outside the weights. The catch is what "state" includes. **Layer 1 — Data (fully portable).** Enterprise documents, tickets, logs. Retrieved via RAG: text is chunked, embedded, stored in a vector database (Pinecone, pgvector), and relevant pieces are fetched into the prompt per query. The embedding model is separate from the LLM, so this layer is genuinely model-agnostic. Already solved. **Layer 2 — Memory (portable in principle).** Systems like Mem0 and Zep sit between the app and the model: after each interaction they extract salient facts ("user prefers X"), store them as plain text, and inject the relevant ones into future prompts. Because the artifact is natural language, it reads into any model. Facts port. **Layer 3 — Orchestration/routing (works, but only for shallow tasks).** Gateways like OpenRouter and LiteLLM normalize API differences and route each request to the cheapest capable model. This is the fungibility layer being furiously built. It genuinely works for one-shot, verifiable tasks — classification, extraction, summarization — which conveniently are the tasks where cheap models suffice anyway. **Where it breaks — the non-portable state.** Four things stay behind when you switch: - **The harness.** Prompts, tool schemas, and guardrails are tuned to one model's quirks. An agent must get every step right, so reliability compounds: a model that's 98% reliable per step completes a 50-step task about a third of the time; at 90% per step, it almost never finishes. Swapping models costs you a few points per step — the difference between an agent that works and one that doesn't. - **The evals.** Swapping means re-testing everything and re-fixing every regression. The real switching cost isn't data migration — it's re-verification. Nobody has abstracted that. - **Procedural memory.** Facts port; skills don't. Cached successful workflows and learned workarounds are conditional on the model that produced them. - **Cache pricing.** Provider-specific, worth 75–90% of input costs on agentic workloads. Quiet lock-in. **The labs' angle.** They offer hosted memory, hosted file stores, caching, fine-tuning — every one pulls state from your side onto theirs. The labs will crack memory first, but as lock-in, not portability. Nobody standardizes their own exit door. MCP is the partial exception: it standardizes tool and data access across models, but doesn't touch harness tuning or evals. **Where 3P vendors fit.** Routers are thin-margin commodity plumbing; vector DBs and memory infra are real but small. The two structurally interesting positions: **eval platforms** (LangSmith, Braintrust) — since switching cost equals re-verification cost, whoever industrializes cross-model testing actually enables fungibility.