Electronic (Danger Zone Mix) is on ELECTRONIC EP2 (DIGITAL EP) 2013 LYS 026
#acidhouse #acidhousemusic #303 #tb303 #roland303 #acidtechno #acidvibes #acidgroove #acidlife #acidnation #warehouseparty #raveculture #ravehistory #oldschoolrave #acidrave #undergroundmusic #electronicmusic #housemusic #technohouse #earlyrave #ukrave #manchesterrave #chicagohouse #detroithouse #electroculture #acidart #acidvisuals #acidstyle #rephlex #afx #acidlegend
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26 results for “warehousepg”
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Police raid a medicines warehouse. Armed Hezbollah supporters reportedly surround them. The army is called.
As France heads to Beirut, who decides where Lebanon’s police can operate?
https://www.thelevantfiles.org/2026/10/lebanons-battle-over-who-gives-orders.html
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[AURORA] - Ransomware Victim: Adams Warehouse & Delivery, Inc[.] - https://www.redpacketsecurity.com/aurora-ransomware-victim-adams-warehouse-delivery-inc/
#aurora #dark_web #data_breach #OSINT #ransomware #threatintel #tor
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Package stuck @ Delhivery Kalamassery Warehouse
https://www.reddit.com/r/Kochi/comments/1x76s3o/package_stuck_delhivery_kalamassery_warehouse/ -
📰 Russians attack warehouse building twice in Kyiv’s Sviatoshynskyi district in evening
🔗 https://censor.net/en/news/4028651/russian-drones-hit-kyiv-warehouse-twice-causing-fire
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3 Reasons Growth Investors Will Love Chefs’ Warehouse (CHEF) https://www.diningandcooking.com/chefs/10658/ #401k #business #BusinessNews #Chef #economy #Finance #FinancialInformation #Investing #InvestmentTools #Investor #MarketNews #mortgage #MutualFunds #PersonalFinance #quote #RealEstate #retirement #stock #StockResearch #StockValuation #stocks #SuzeOrman #tax #TrackPortfolio
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Fake weight-loss jabs worth £2m seized in Birmingham warehouse raid
The medicines were found in a “filthy warehouse” and had never been checked for their safety, quality or…
#NewsBeep #News #Medication #AU #Australia #Health
https://www.newsbeep.com/au/932107/ -
Fake weight-loss jabs worth £2m seized from 'filthy Birmingham warehouse'
https://fed.brid.gy/r/https://www.mirror.co.uk/news/uk-news/weight-loss-injections-reta-jab-37778965
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Living in a bookshop is like living in a warehouse of explosives. Those shelves are ranked with the most furious combustibles in the world--the brains of men.
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https://www.europesays.com/uk/1259654/ Landlord doubles down on Aldridge estate with multimillion-pound warehouse buy #Birmingham #Britain #England #GreatBritain #UK #UnitedKingdom
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"Abolish & Replace ICE": Rep. Analilia Mejía Blasts ICE for Secretly Using NJ Warehouse http://www.democracynow.org/2026/10/16/new_jersey_ice_warehouse
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🚨New ransom group blog post!🚨
Group name: aurora
Post title: Adams Warehouse & Delivery, Inc.
Location: 🇺🇸 US
Sector: Other
Info: https://cti.fyi/#disclosure/aurora/Adams%20Warehouse%20%26%20Delivery%2C%20Inc.#ransomware #cti #threatintelligence #cybersecurity #infosec
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NOW ON VERDANT SQUARE RADIO ==
NOW PLAYING == Democracy Now! 10/16/26: Rep. Analilia Mejia Blasts ICE for Secretly Using NJ Warehousehttps://youtu.be/AsMomg_kRuo?si=sWOAO1zGxjZWm7pE#news #war #peace #uspol #geopolitics #antiwar #journalism #elections #democracynow #AmyGoodman #JuanGonzález #NermeenShaikh #vsn #radio #SupportIndependentMedia #DiverseSpectrumOfTheLeft #Resist
https://mastodon.social/@VerdantSquareRadio/117451225619086284
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NOW PLAYING == Democracy Now! 10/16/26: Rep. Analilia Mejia Blasts ICE for Secretly Using NJ Warehouse
https://youtu.be/AsMomg_kRuo?si=sWOAO1zGxjZWm7pE
#news #war #peace #uspol #geopolitics #antiwar #journalism #elections #democracynow #AmyGoodman #JuanGonzález #NermeenShaikh #vsn #radio #SupportIndependentMedia #DiverseSpectrumOfTheLeft #Resist
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£2m fake weight-loss pens found in 'filthy' Birmingham hub – BBC
£2m fake weight-loss pens found in ‘filthy’ Birmingham hub BBCGangs exploiting boom in weight-loss jabs The TelegraphOne warehouse, 50,000 fake…
#NewsBeep #News #US #USA #UnitedStates #UnitedStatesOfAmerica #Medication #Health
https://www.newsbeep.com/us/884216/ -
The ultimate guide to Frieze London 2026
This week, the city is alive with art not just in every gallery, but every church, every warehouse,…
#London #Britain #UnitedKingdom #UK #GB #England #Headlines #News #Europe #EU #Frieze #Friezeartfair #regentspark
https://www.europesays.com/gbr/london/40124/ -
Seven Years After FDA Warned of Rodents, Gold Star Finally Gets Shut Down
In 2018, FDA warned Gold Star Distribution that rodent contamination could lead to an injunction. Seven years and 26 inspections later — with nine live rodents still in the warehouse — it finally did. -
FDE‑Gym — The Last Mile of AI Delivery Gets Real‑World Validation
Lead
“Two‑minute‑late deliveries cost $1.3 million a year for our fleet,” a senior engineering director at a leading autonomous‑vehicle company told us in an exclusive interview last week. The same executive added that the team’s new validation pipeline—built around FDE‑Gym—cut that loss in half. The headline‑grabbing numbers come from a benchmark that deliberately throws the kind of vague, safety‑critical, on‑device constraints that traditional AI tests ignore. In short, FDE‑Gym forces engineers to turn a customer’s “make it work” into a production‑ready, edge‑deployed solution—the exact problem that stalls many “AI‑first” promises in the field.
The Case Study: From Ambiguous Request to Edge‑Ready Delivery Bot
Imagine a logistics client that asks for “temperature‑controlled last‑mile delivery” for a new line of organic meals. The request lacks specifics: What temperature range? How long can the vehicle stay idle in a hot warehouse? Does the client care about battery consumption?A forward‑deployed engineer (FDE) at FleetAI receives the request via the company’s ticketing system. The engineer opens the FDE‑Gym “Cold‑Chain” scenario, which mirrors this exact ambiguity. The benchmark presents a structured dialogue: the engineer must ask clarification questions, capture the client’s answers, and encode them as constraints in the model pipeline.
- Requirement Clarification – The engineer queries the client through the simulated chat interface. The client replies: “Maintain 2 °C ± 0.5 °C for up to 4 hours, and never drop below 1 °C, even if ambient temperature hits 35 °C.”
- Model Selection & Compression – The benchmark forces the engineer to pick a tiny quantised transformer that fits the edge ASIC’s 2 GB RAM limit and consumes less than 5 W. The engineer applies post‑training quantisation and structured pruning, reducing parameters from 45 M to 31 M while preserving the temperature‑control accuracy.
- Safety Injection – FDE‑Gym injects a sensor‑dropout event mid‑mission. The engineer’s code must detect the dropout, switch to a fallback PID controller, and log the event for post‑mission audit.
- On‑Device Learning – The scenario includes a few‑shot adaptation where the bot learns the thermal inertia of a new insulated container after the first delivery. The engineer implements a tiny LoRA‑style adapter that updates on‑device without sending raw temperature data to the cloud, satisfying GDPR‑style constraints.
After the engineer finishes the loop, the benchmark scores the solution on four axes: inference latency, energy per inference, safety‑incident risk, and clarification‑cycle time. The final report shows a 94 ms median latency, 4.1 mJ per inference, and a 60 % reduction in human clarification time compared with the team’s legacy process.
The same workflow now runs automatically in FleetAI’s continuous‑learning pipeline. Each new client request triggers a fresh FDE‑Gym run, producing a verified, edge‑ready artifact that ships to the vehicle within hours instead of days.
The Meat: Hard Numbers Across Pilots
MetricBaseline (pre‑FDE‑Gym)Post‑FDE‑GymΔWhy the shift mattersInference latency (median)120 ms94 ms‑22 %Faster decisions reduce route jitter and improve safety margins.Model size (effective parameters)45 M31 M‑31 %Smaller models free memory for additional sensors and enable retrofits on legacy hardware.Energy per inference6.4 mJ4.1 mJ‑36 %Lower energy extends battery life by ~15 %, translating to more miles per charge.Clarification cycles per new task4.2 h1.7 h‑60 %Engineers spend less time interpreting vague requests, accelerating time‑to‑market for new services.Safety‑incident rate (critical failures / 10 k miles)3.21.1‑66 %Directly impacts regulatory compliance and brand trust.On‑time delivery rate78 %92 %+14 ppHigher reliability improves customer satisfaction and reduces penalty fees.Human‑in‑the‑loop interventions2.3 per 1 k deliveries0.9 per 1 k deliveries‑61 %Fewer manual overrides cut operational cost and free engineers for higher‑value work.
Below we aggregate performance data from four independent pilots that adopted FDE‑Gym in the last six months. The pilots span autonomous delivery bots (US, EU), warehouse robotics (Asia), and field service drones (Australia).Key stat: After integrating FDE‑Gym, FleetAI’s autonomous fleet lifted its success‑rate on ambiguous delivery requests from **78 % to 92 %* within a two‑month field test.*
The data reveal a consistent pattern: embedding the “last‑mile” constraints into the benchmark forces teams to adopt model‑compression, uncertainty‑aware inference, and rigorous safety testing early in the development cycle. Those practices, in turn, generate measurable efficiency gains across the board.Edge‑AI Performance Gains Explained
- Iterative Clarification Loop – By treating requirement gathering as a first‑class engineering step, FDE‑Gym pushes engineers to instrument models with confidence scores. The resulting uncertainty estimates enable early aborts or fallback strategies, cutting latency spikes caused by indecision.
- Hardware‑Constrained Tasks – The benchmark’s explicit RAM and power caps discourage “train‑big‑then‑prune” pipelines that waste compute. Engineers instead design from the bottom up, selecting architectures such as MobileViT, TinyBERT, and Edge‑Optimised CNNs that meet the constraints without excessive post‑hoc compression.
- Failure‑Injection Scenarios – Simulated sensor dropouts, network latency spikes, and adversarial inputs force developers to code defensive pathways (e.g., model‑agnostic controllers, watchdog timers). Those pathways survive deployment, reducing safety‑incident rates dramatically.
The Pivot: Risks and Regulatory Headwinds
While the performance uplift looks impressive, the last‑mile nature of FDE‑Gym also surfaces new challenges that companies cannot ignore.1. Ambiguity Liability
Regulators across the EU and the US tighten scrutiny on AI systems that interpret vague client instructions. The EU AI Act classifies “high‑risk AI” that makes safety‑critical decisions, and it now asks for traceability of requirement interpretation. If an autonomous bot misreads “keep the package cool” and delivers a hot pizza, the liability chain becomes murky.Mitigation via FDE‑Gym: Every clarification dialogue the benchmark records as a structured JSON log. Companies can attach those logs to safety dossiers, providing auditors with a clear audit trail that shows the engineer asked the right follow‑up questions and documented the client’s answers.
2. Safety Certification Gaps
Traditional functional‑safety standards (ISO 26262, IEC 61508) assume deterministic software. AI models, especially those that adapt on‑device, break that assumption. Certification bodies still lack standard test vectors for dynamic AI behaviour.Mitigation via FDE‑Gym: The benchmark’s failure‑injection library—including sensor loss, adversarial perturbations, and power‑capping events—offers a de‑facto standard. Engineers can reference the exact test cases in their safety case, demonstrating that the AI system survived the same stressors the certification process demands.
3. Data‑Privacy Friction
Edge AI often learns from on‑board telemetry that includes personal data (delivery addresses, temperature logs of food). New proposals under GDPR‑like AI data‑governance rules require that any on‑device learning does not transmit raw personal data.Mitigation via FDE‑Gym: Several scenarios deliberately forbid cloud‑sync of raw sensor streams, forcing engineers to implement privacy‑preserving updates (e.g., federated averaging of model gradients, differential‑privacy noise injection). Those implementations satisfy both the benchmark and emerging legal expectations.
4. Vendor Lock‑In
FDE‑Gym’s code‑loader utilities target Python‑centric stacks and popular edge runtimes (TensorRT, ONNX Runtime). Companies that have already invested in alternative ecosystems (e.g., proprietary DSP toolchains) may face friction integrating the benchmark.Mitigation: The open‑source community already ported the data‑loader to Rust and C++ within two months of the arXiv release. Early adopters report seamless integration after minor adapter layers.
Outlook: Where FDE‑Gym Heads Next
The momentum behind FDE‑Gym suggests a new ecosystem of “last‑mile‑ready” AI tools. Expect the following trends to accelerate over the next 12‑18 months:- Standardised Certification Packages – Certification bodies will likely cite FDE‑Gym’s failure‑injection suite as an annex to safety standards, making the benchmark a prerequisite for any high‑risk edge AI product.
- Marketplace for Pre‑Validated AI Modules – Vendors will publish FDE‑Gym‑certified model bundles (e.g., “Edge‑Optimised Object Detector v1 – 95 % success on FDE‑Gym’s Occlusion scenario”). Buyers can plug those bundles into their pipelines with confidence that they meet both performance and safety thresholds.
- Automated Clarification Agents – Companies will build LLM‑driven assistants that auto‑generate the clarification dialogue required by FDE‑Gym, further shrinking the human‑in‑the‑loop time from hours to minutes.
- Cross‑Domain Benchmark Expansion – Researchers plan to add medical‑device, industrial‑robot, and smart‑grid scenarios to the suite, extending the “last mile” concept beyond logistics.
- Regulatory Adoption – The IEEE P7009 safety‑critical AI working group already references FDE‑Gym in its draft recommendations. Once finalised, the benchmark could become a mandatory compliance artifact for AI systems deployed on public roads.
Closing Thoughts
FDE‑Gym flips the script on AI benchmarking. Instead of measuring how fast a model can classify ImageNet, it measures how well engineers translate a fuzzy business request into a safe, edge‑ready system. The early adopters prove that the approach delivers concrete ROI: lower latency, reduced power draw, fewer safety incidents, and faster rollout of new services.At the same time, the benchmark forces the industry to confront the legal and safety realities that have long lurked behind the hype of “AI‑first” delivery. By insisting on structured clarification logs, failure injection, and privacy‑preserving updates, FDE‑Gym gives companies the evidence base they need to satisfy regulators and protect customers.
If the next wave of autonomous delivery fleets wants to move beyond pilot projects and achieve city‑wide, on‑time, safe operations, they will need a tool that mirrors the messiness of the real world. FDE‑Gym provides exactly that mirror—and the data show that looking into it helps teams clean up their own processes.
Bottom line: The “last mile” of AI delivery is no longer an abstract bottleneck; it is a measurable, improvable stage, and FDE‑Gym offers the first public yardstick to master it.
#ai #edgecomputing #autonomousvehicles #software #coding #development #engineering #inclusive #community
How One Tool Cut $1.3M in Delivery Losses in Just Two Minutes -
“Just like electricians or carpenters, #softwaredevelopers must spend years practicing their craft in order to become skilled and sought-after. It doesn’t necessarily require a formal education, but it certainly takes time and practice to master.
I wonder whether this incorrect assumption is fuelling, at least in part, the current #AI #identitycrisis that software developers are facing. #Bluecollar workers, in general, have successfully ridden the waves of technological change. If you can manage a crew of manual carpenters, you can also manage a warehouse full of woodworking robots. It’s usually the #whitecollar workers who bear the most economic disruption from major technological innovations.
#Software engineers are discovering that, just like anyone else in a skilled trade, they actually can be outpaced by technology. So we do what other skilled traders have done: figure out how to use the new technology to do the jobs we used to do by hand.”
https://ptrbrynt.com/posts/were-we-blue-collar-workers-all-along
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[#TRADESHOW] #CeMAT #ASIA 2026 will be held from November 3 to 6, 2026, at #Shanghai New #International #Expo #Center, #China. As a leading #logistics #trade #exhibition in Asia-Pacific, the #event serves as a major #B2B #business #platform for material #handling, warehouse #technology, intralogistics #systems, and supply chain #solutions.CeMAT ASIA has grown into a major annual #meeting point for the regional logistics #industry. https://cnbusinessforum.com/event/cemat-asia-2026/
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Ahoj, přináším další #rockforcoffee podle vaší volby✌️🎸
Výročí rockových a metalových desek ❤️💙🤟
#narozeniny slavných rockových alb 365/61(šedesát jedna)
Z mnoha alb, která vyšla 17. října jsem vybral tato čtyři 👇
🔥Pretty Maids - Red, Hot and Heavy (1984)
🎸KISS - Hot in the Shade (1989)
⚡️AC/DC - Black Ice (2008)
❄️Iced Earth - Dystopia (2011)Podle ankety je vítězem skupina AC/DC a jejich album “Black Ice” z roku 2008✌️🎸
💿 “Black Ice” je návratem australských rockových legend po dlouhých osmi letech a zároveň poslední studiové album AC/DC, na kterém hrál zakládající rytmický kytarista Malcolm Young…
Deska vyšla v Evropě 17. října 2008, v Austrálii 18. října a na řadě dalších trhů včetně USA 20. října. 15. studiové album vyšlo u vydavatelství Columbia Records a produkoval jej Brendan O’Brien. Nahrávalo se březen–duben 2008, ve Warehouse Studio v kanadském Vancouveru…
Předchozí album “Stiff Upper Lip” vyšlo v roce 2000. Osmiletá pauza byla pro AC/DC neobvykle dlouhá. Kapela ale nepřišla s novou hudební identitou ani se nepokoušela dohánět módní trendy. Místo toho nabídla patnáct nových písní postavených na tom, co vždycky uměla nejlépe. Riffy, bluesový základ, přímočarý rytmus a refrény, které se dají řvát společně s kapelou…
Sestava na albu:
Angus Young - sólová kytara🎸
Jeho charakteristický zvuk kytary Gibson SG je jedním z nejrozpoznatelnějších v rockové historii. Na Black Ice zůstává věrný bluesovým kořenům, ale umí přidat i hutnější a temnější tón.Malcolm Young - rytmická kytara🎸
Skutečný architekt zvuku AC/DC. Jeho přesné, úsporné riffy a pevné akordy vytvářejí základ, na němž Angus staví sóla. Malcolmův přínos je na této desce obrovský, každý riff působí jednoduše, ale má správný tah a groove.🧶 1/4
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🏆 New Achievement! One Firewall To Fool Them All!
Step right up, valued customer! May I interest you in our Premium Post-Compromise Audit Package? No? Well, Unit 42 has confirmed that attackers exploited CVE-2024-47575 in FortiManager's FMG-Access channel, dropped the PEERSTITCH implant, and used a single compromised appliance to exfiltrate sensitive data from over 500 FortiGate firewalls. Five hundred! That's efficiency even a discount warehouse would respect. (1/2)
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Most order systems store one boolean for cross-border shipments, something like
is_international, and then let a human figure out the paperwork. 19 CFR part 143 does not behave like a boolean. It keys three different things off three different facts, namely which class of entry is available, who is permitted to sign it, and which document has to exist. A single flag cannot carry that, which is why the same column produces so many surprises at a port.Three facts, three columns
create table inbound_shipment (
shipment_id text primary key,
entered_value_cents bigint not null, -- value of the portion being entered
landed_at timestamptz, -- carrier event, not purchase order
party_role text not null check (party_role in
('owner','purchaser','consignee')),
broker_designated boolean not null default false,
entry_class text check (entry_class in
('formal','informal','none_yet')),
entry_form text check (entry_form in
('cbp_368','cbp_368a','cbp_7501','electronic','invoice_with_declaration')),
declared_at timestamptz
);entered_value_centsis where the modelling habit goes wrong first. The $2,500 figure in 143.21 applies to a shipment, and the same section reaches an installment arriving at a different time plus a separable portion of one consignment under 141.52. So the number that matters is the value of the piece being entered, not the value of the container it came off. A column fed by a purchase-order total will be wrong for exactly the consolidated inbound flows that make consolidation attractive.landed_atis the second one. 141.5 sets the deadline at 15 calendar days from landing off a vessel, aircraft or vehicle, or from arrival at the destination port where goods moved in bond. Calendar days, and the trigger is a carrier event. If your only date is when the goods were bought or when they shipped, the deadline cannot be computed at all, and a null here is the honest answer until a carrier feed fills it.The signer is not a constant
143.26 splits by band, and the split changes which of your own parties can act.// 19 CFR 143.26, read as a guard rather than a lookup.
export function maySelfEnter(role: 'owner' | 'purchaser' | 'consignee', valueUsd: number) {
if (valueUsd > 2500) return false; // outside the informal bands in 143.21
if (valueUsd > 800) return role === 'owner' || role === 'purchaser';
return true; // owner, purchaser or consignee
}In the band above $800 and up to $2,500 the permitted filers are the owner and the purchaser, or a broker licensed under 19 U.S.C. 1641 acting on a designation from owner, purchaser or consignee. Below $800, with 19 U.S.C. 1321(a)(2) satisfied, the consignee is added to that list. A warehouse that receives as consignee therefore sits on different sides of that line depending on a value it does not set, and a system that models "who can file" as one role constant will keep getting that wrong.
Both paragraphs carry the phrase using reasonable care, which attaches to the person signing. That is a standard rather than a field, but it has a data consequence, since reasonable care is easier to demonstrate with a document trail than with a recollection.
Eligibility is revocable, and mergeable
Two sentences in 143.22 break any pure decision table. A formal consumption or appraisement entry can be demanded for any merchandise on three grounds, namely admissibility enforcement, revenue protection, or the efficient conduct of customs business. Shipments for one consignee at or under $2,500 can also be pulled together onto a single formal entry.create table entry_decision (
shipment_id text references inbound_shipment,
decided_by text not null, -- system | cbp_request | consolidation
forced_formal boolean not null default false,
parent_entry text, -- set when shipments merge onto one entry
decided_at timestamptz not null
);The column worth adding before you need it is
parent_entry. Once shipments merge, the value band that made informal entry available stops describing the filing, and a report that still shows two eligible entries will disagree with the one that was made.The declaration is an artifact, store it
Where a center director authorizes it, 143.23 permits an informal entry on a commercial invoice carrying a signed declaration, and the wording is fixed. It covers accuracy to the signer's knowledge and belief, invoice quantities matching manifest quantities, and the absence of any other invoice received or known. That third clause is a claim about your own documents, so the row should record which document was signed and by whom, not merely that a filing happened. 143.27 wants the invoice for the transaction itself, and only falls back to an itemized statement of value when no invoice exists, which is a second artifact with a reason attached to it.create table entry_document (
shipment_id text references inbound_shipment,
kind text not null check (kind in
('commercial_invoice','itemized_value_statement','declaration_signed')),
signed_by text,
reason_no_invoice text, -- why the statement replaced the invoice
stored_at timestamptz not null default now()
);A power of attorney sits behind all of this. 141.31 lets the instrument be drawn for a single transaction or for the whole of a principal's customs work, and rejects one running to a minor, so scope and expiry are fields rather than a folder.
None of this is legal advice, and a filing decision belongs to whoever signs it. What a schema can do is stop hiding the three facts the rule keys off, which are value of the portion, landing, and role. FulfillNexa by SBT (fulfillnexa.com) runs inbound for sellers at three Chinese sites, the Suzhou floor measuring 13,000 square meters, Dongguan 8,000 and Shenzhen 3,000, and we are never the signer on an entry. The
landed_atand the value we were told at the door are our rows, and they are the two fields most often missing when someone else has to explain a filing months later.#database #backend #ecommerce #compliance #software #coding #development #engineering #inclusive #community
An entry type is a row with a value band and a signer -
Residents appeal #GorhamME 's approval of #AmazonWarehouse
by Nicole Ogrysko, Oct 16, 2026
Excerpt: "A group of Gorham residents are appealing a recent decision by the town Planning Board to greenlight a 146,000-square-foot Amazon distribution facility.
"The residents filed an appeal with the Cumberland County Superior Court. They argue that the site plan approved last month does not address their longstanding concerns about the project’s #environmental and #traffic impacts.
" 'Our reason for getting into this fight is that we care about our community,” said Josh Trombley, an organizer with Gorham United, which filed the appeal. 'We think that this project is not a good fit, that it’s going to bring a lot of significant negative impacts.'
"The residents are asking the court to overturn the Gorham Planning Board approval. They argue that the site plan does not address town development codes and land use standards on traffic, road capacity, access, #stormwater issues, #noise and other impacts.
Archived version:
https://archive.ph/2CwmH#MainePol #NoisePollution #Traffic #IncreasedTraffic #InducedSprawl #GorhamConnector #NoAmazonWarehouse
#NoGorhamConnector -
TL;DR — Columnar engines like Arrow, DuckDB, Polars, and GPU dataframes are built for scan-heavy, amortized access — ideal for building training sets, terrible for single-row inference lookups. The real value of Arrow in AI systems isn't query speed, it's zero-copy interchange between the offline columnar world and the online row-oriented world. Teams that try to make one engine do both pay for it in p99 latency.
Every few months another benchmark lands showing DuckDB or Polars or a GPU dataframe engine crushing some TPC-H-style workload. The numbers are real. The conclusion people draw from them — that the columnar engine is now the obvious backbone for AI data infrastructure, end to end — is where things go wrong.Columnar engines are extraordinary at one thing: scanning a lot of rows to compute a few aggregates. They are structurally bad at the thing most AI inference paths actually need, which is fetching one row, fast, under load. Those are not the same problem, and the industry keeps building systems that quietly assume they are.
What "columnar" is actually optimizing for
Column stores exist because analytical queries touch a small number of columns across a huge number of rows. Storing data column-wise means you only read the bytes you need, you compress similar values together, and you can vectorize operations across contiguous memory with almost no branching. This is why Arrow's columnar memory format, DuckDB's execution engine, and GPU dataframe libraries all converge on the same core bet: amortize a fixed overhead over millions of values.That bet is exactly right for building a training set. You're joining a fact table of events against a dimension table of user attributes, computing rolling aggregates, filtering, deduplicating — all scan-and-reduce operations over large batches. A columnar engine will do this an order of magnitude faster than a row-oriented database, and GPU-accelerated columnar execution pushes that further by parallelizing the same vectorized operations across thousands of cores.
None of this has anything to do with what happens at inference time.
Where the model breaks
An inference request — a chat turn, an agent step, a ranking call — needs a handful of specific rows: this user's current feature vector, this document's embedding, this session's running state. That's a point lookup, not a scan. In a column store, reconstructing a single logical row means touching N separate column chunks, each potentially compressed, each potentially on a different page or file. You pay that reconstruction cost whether you need one row or a million. Over a million rows, it disappears into the vectorized throughput. Over one row, at p99, under concurrent load, it's pure latency with no amortization to hide behind.This is why nobody serves a production recommendation feature store out of a Parquet file scanned fresh every request. It's why vector databases don't store vectors in a generic columnar table and brute-force scan them — they build HNSW or IVF indexes specifically because the access pattern is "find me the nearest neighbors to this one query vector right now," which is the opposite of what columnar layouts are good at. The vector database world already learned this lesson. A lot of the broader AI data stack hasn't internalized it yet.
You see the same mistake show up in agent systems. An agent step that needs to read conversation state, tool outputs, and a slice of long-term memory is doing dozens of small, latency-sensitive point reads per turn. Bolt a columnar analytics engine underneath that and you've built a system that is blazingly fast at answering "how many agent runs happened last week" and painfully slow at answering "what did this specific agent remember five turns ago."
Arrow's real contribution isn't the query engine
This is where Arrow actually earns its place, and it's not the part most benchmarks celebrate. Arrow's contribution to AI infrastructure is a shared, zero-copy, in-memory columnar layout that multiple engines can read and write without serializing between them. Pandas, Polars, DuckDB, Spark, a PyTorch dataloader, a GPU dataframe — all of them can hand off the same block of memory without a conversion tax at every boundary.That matters enormously for the offline side of an AI pipeline, where data moves through five or six tools on its way from raw logs to a training batch. Every serialization boundary you remove is real wall-clock time and real infrastructure cost returned to you, independent of how fast any single engine's query planner is. The headline-grabbing part of the Arrow ecosystem is "look how fast this SQL query runs." The quietly load-bearing part is "look how many copy operations this eliminated between tools nobody thought to combine."
Conflating those two contributions is the mistake. Teams read "Arrow is fast" and "DuckDB is fast" and conclude the columnar stack should extend all the way to the serving boundary. It shouldn't, and trying to force it there is how you end up with an inference path that's fast in a load test with batch size 10,000 and falls apart in production where real traffic looks like batch size one, repeated constantly, with a latency budget measured in milliseconds.
Designing for two speeds, not one
The architecture that actually works treats this as two distinct problems joined at a well-defined seam. Offline, use the columnar, GPU-accelerated stack for exactly what it's good at: building and refreshing training datasets, computing batch feature transforms, backfilling, running analytical evaluation over logged inference traffic. This is where Arrow-based engines and GPU dataframes deliver real, repeatable wins, because the workload genuinely is scan-and-reduce over large volumes.Online, use a row-oriented store, a key-value layer, or a purpose-built index — whatever gives you single-digit-millisecond point lookups at your concurrency target. The feature store pattern of an offline columnar warehouse paired with a low-latency online store isn't legacy cruft to be modernized away by a faster columnar engine; it's the correct shape of the problem, and GPU acceleration on the columnar side doesn't change which side of the seam a given workload belongs on.
The seam itself is where Arrow should live — as the interchange format that lets your offline columnar pipeline hand off a batch of freshly computed features to whatever is populating the online store, without a serialization detour through JSON or a bespoke binary format. That's a real, meaningful win. It's just a different win than "#ai #llm #python #agents #software #coding #development #engineering #inclusive #community
DuckDB and Arrow Win AI Data Prep, Then Hit a Wall at Serving
