SShortSingh.
Back to feed

How to Copy HubSpot Properties Between Accounts: Native Options and Limits

0
·3 views

HubSpot has no built-in button to copy a set of properties between accounts, though its newer tools carry properties along in certain cases. For testing, an Enterprise user can create a sandbox that copies most property setups from production, though some items such as certain dropdowns, access settings and integration-created groups are excluded. Multi-account management and Partner Portability can copy assets between production accounts, but properties only come along as part of those assets rather than as a standalone set. For a plain copy of contact, company, deal or ticket properties, the practical route is to export the property list from the source account and recreate groups and properties manually in the target. This manual method is free and works on all plans, but it takes more effort, especially for dropdown options.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

Developer builds Supabase connector for Switch to bring AI agents into team chat

A developer built a Supabase connector for Switch during the Flip the Switch hackathon, letting AI agents work in team chat rooms instead of isolated terminals. The connector was simple to add, with the platform handling login flow and scopes, and it works across multiple agents and chat platforms. The developer's demo workflow had a triager agent diagnose database bugs from logs and a second agent propose SQL fixes, with a rule that no change runs without explicit human approval of the exact statement. The author noted a limitation: Switch does not yet distinguish read-only from write tools, so that guardrail currently lives in the developer's own skill rather than the platform.

0
ProgrammingDEV Community ·

Editing 0.016% of a model's weights reduces looping on math tasks

Researchers tested a targeted change to the weights of Qwen 3.5, an 800-million-parameter model that was getting stuck in repetitive loops on math exercises. The edit was made without any new training or backpropagation. On the MATH-500 benchmark, the authors report a score rising from 7.9% to 48.1%, and a later public re-evaluation on Hugging Face reported 51%. The result depends on a specific protocol, and reducing generation loops does not by itself prove a general improvement in the model's mathematical ability. A second experiment is expected to add nuance to these findings.

0
ProgrammingDEV Community ·

Privy, Turnkey and Coinbase CDP Differ on Agent Wallet Spend Caps

A comparison of agent wallet policy engines from Coinbase CDP, Privy and Turnkey shows they handle unmatched operations differently. CDP accepts any operation that no policy mentions, so a spend cap on one signing method can leave another method unlimited. Privy and Turnkey instead deny operations that are not explicitly allowed. The author notes that top search results for these vendors do not compare their policy formats or whether they support daily spend caps. The comparison is based on each vendor's documentation, OpenAPI specs and pricing pages as of 7 November 2026. The article also finds that only Turnkey offers a public rolling spend cap feature.

0
ProgrammingDEV Community ·

RecallOps Pairs PostgreSQL With Long-Term Agent Memory to Improve Incident Response

Engineers often write detailed postmortems that go unread, so similar outages later lead to repeated failed fixes. The author's team built RecallOps, an operational intelligence layer for the Kartly e-commerce platform, to remember past incidents including alerts, recent deployments, attempted fixes, and what restored service. Their design combines a PostgreSQL store for structured operational state with Hindsight, a long-term memory engine that organizes distilled postmortems into memory banks with temporal and entity reasoning. The team argues that naive retrieval-augmented generation over raw logs performs poorly, because redundant log lines obscure causal signals, plain embeddings cannot tell successful fixes from harmful ones, and vector search loses incident chronology.